Triggering collection of consumer input based on location data

ABSTRACT

A method and system for gathering consumer feedback and information based at least in part on location data relating to movement of a consumer. A method and system are provided for gathering opinions and feedback from and having interactions with consumers to collect consumer responses to tasks requested by the system. The system may request that a consumer perform a task based on an analysis of the electronically-derived consumer location data as well as previously captured opinions and feedback. In addition, location-derived insights can be used to segment and further inform and refine the information gathered from consumers.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. §119(e) to U.S.Provisional Patent Application Ser. No. 61/501,581, filed on Jun. 27,2011, and titled “Timing collection of consumer input based on locationdata,” which is incorporated herein by reference in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable

REFERENCE TO SEQUENCE LISTING, A TABLE, OR A COMPUTER PROGRAM LISTINGCOMPACT DISK APPENDIX

Not Applicable

BACKGROUND

1. Field of the Invention

Embodiments of the invention relate to systems for analyzing andgathering information on and/or from consumers. More specifically,embodiments of the present invention are directed to techniques forcollecting relevant and timely data from and about consumers to makeinferences and predictions in combination with electronically-capturedlocation data.

2. Discussion of Related Art

Businesses can often benefit from knowledge about the behavior of theircustomers or prospective customers. For example, a business may offercertain products or undertake a marketing strategy based on its beliefsregarding who its customers are. If these beliefs are inaccurate,though, the business' efforts may be misdirected and the business mayfail to maintain old customers or attract new customers.

Efforts have been previously made at collecting information aboutconsumers who may be customers and prospective customers of a business.In some such techniques, a researcher may ask consumers about theiridentities, preferences or behaviors using direct questioning. Thesequestions may be designed to solicit particular information aboutconsumers, such as regions in which a business' customers live, asocioeconomic grouping of consumers, how often the consumers shop at thebusiness, factors influencing purchasing decisions, and their consumingpreferences. Written or oral questionnaires, one-on-one interviews,brief point-of-sale questions at the business, focus groups, andtelephone or online surveys are examples of ways in which informationabout consumers can be collected using direct questioning.

This same information may be voluntarily provided by consumers when theconsumers register for a service. This may be the case when consumersare registering for discount programs or for services offeredcommercially by the business. Thus, when a consumer subscribes toservices offered by the business, direct questions may solicitinformation that may be used to acquire information about the individualconsumer and for the general class of that business' consumers. Theacquired information may then be analyzed to determine informationuseful to the business.

SUMMARY

In one embodiment, there is provided a method of processing a pluralityof units of location data for a plurality of consumers to determinewhether to solicit information relating to commercial activity. Thelocation data identifies a plurality of locations of the plurality ofconsumers at a plurality of times. The method comprises determining,using at least one processor and based at least in part on the pluralityof units of location data for the plurality of consumers, at least onebehavior for one or more of the plurality of consumers and, in responseto determining that the at least one behavior satisfies at least onecondition to solicit the information, sending at least one message to aconsumer soliciting information relating to commercial activity.

In another embodiment, there is provided a method of determining, basedon location data, whether to solicit information relating to commercialactivity. The method comprises receiving, over a period of time, aplurality of units of location data. The plurality of units of locationdata identify a plurality of locations of the plurality of consumers ata plurality of times. The plurality of units of location data comprisemultiple measurements of a physical location of a first consumer. Themethod further comprises analyzing the plurality of units of locationdata, producing a result of the analysis, and determining, using atleast one processor, whether the result of the analysis satisfies acondition for information to be solicited. The method further comprises,in response to determining that the result satisfies the condition,sending at least one message to a consumer soliciting informationrelating to commercial activity of the first consumer.

In a further embodiment, there is provided an apparatus comprising atleast one processor and at least one storage medium having encodedthereon executable instructions that, when executed by the at least oneprocessor, cause the at least one processor to carry out a method ofprocessing a plurality of units of location data for a plurality ofconsumers to determine whether to solicit information relating tocommercial activity. The location data identifies a plurality oflocations of the plurality of consumers at a plurality of times. Themethod comprises determining, using at least one processor and based atleast in part on the plurality of units of location data for theplurality of consumers, at least one behavior for one or more of theplurality of consumers. The method further comprises, in response todetermining that the at least one behavior satisfies at least onecondition to solicit the information, sending at least one message to aconsumer soliciting information relating to commercial activity.

In another embodiment, there is provided a method of operating aportable computing device. The method comprises obtaining a plurality ofunits of location data, each of the plurality of units of location dataindicating a location of a consumer determined as the consumer movedwhile engaging in at least one behavior, transmitting the plurality ofunits of location data to at least one server, and receiving, from theat least one server, at least one message soliciting informationrelating to commercial activity, the at least one message having beentransmitted by the at least one server at least partly in response tothe at least one server determining the at least one behavior of theconsumer based at least in part on the plurality of units of locationdata. The method further comprises receiving, from the consumer via auser interface of the portable computing device, the informationsolicited by the at least one message and transmitting the informationreceived from the consumer to the at least one server.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are not intended to be drawn to scale. In thedrawings, each identical or nearly identical component that isillustrated in various figures is represented by a like numeral. Forpurposes of clarity, not every component may be labeled in everydrawing. In the drawings:

FIG. 1 illustrates one exemplary environment in which embodiments mayoperate;

FIG. 2 is a flowchart of an exemplary process for triggering actionsthat gather information from and/or on consumers;

FIG. 3 is a block diagram of an exemplary computing device with whichembodiments may operate;

FIG. 4 is a flowchart of an example of a process for determining asetting visited by a consumer based on location data obtained for theconsumer;

FIG. 5 is a flowchart of an example of a process for requesting that aconsumer perform a task in response to an evaluation of location datafor the consumer;

FIG. 6 is a flowchart of an example of issuing a request to a consumerto perform a task in response to an evaluation of location data for theconsumer;

FIG. 7 is an exemplary image of the result of an action which displaystwo survey questions for a consumer to answer on a device;

FIG. 8 is a flowchart of another example of issuing a request to aconsumer to perform a task in response to an evaluation of location datafor the consumer;

FIG. 9 is a flowchart of an example of a process for requesting datafrom one or more external data stores in response to an evaluation oflocation data for the consumer;

FIG. 10 is a flowchart of an example of a process for transmitting datato one or more external data stores in response to an evaluation oflocation data for the consumer; and

FIG. 11 is a flowchart of an example of a process for adjusting a visitdetection process based on information received from a consumer and/oran external data source identifying a setting visited by a consumer.

DETAILED DESCRIPTION

The Applicants have recognized and appreciated various improvements thatmay be made in a consumer analytics system from using, in combination,data revealing behaviors of consumers that are related to or inferredfrom location of the consumers, and other types of information. Suchlocation-based behaviors and other types of information may becorrelated in time such that the data relating to the behavior and theother types of information may be contemporaneous.

In one aspect, Applicants have recognized and appreciated the advantagesof detecting consumers' behaviors contemporaneously with the consumersengaging in the behaviors and collecting information in response todetecting those behaviors. More particularly, Applicants have recognizedand appreciated the advantages of a consumer analytics system that mayinfer and/or predict consumers' behaviors from evaluating physicallocations through which the consumers passed. Such a system may triggercollection of information regarding commercial activity based on thosebehaviors.

Accordingly, described herein are techniques for operating a consumeranalytics system to obtain location data for consumers and producepredictions and/or inferences of characteristics of consumers, which mayinclude predictions and/or inferences of behavior characteristics ofconsumers relating to behaviors of the consumers. The consumer analyticssystem may be configured to take one or more actions when one or moreconditions for the actions are satisfied. At least some of theconditions may relate to characteristics of consumers. Whencharacteristics of consumers predicted and/or inferred by the consumeranalytics system satisfy the condition(s) for an action, the consumeranalytics system may take the action. In some embodiments, the consumeranalytics system may determine the characteristics of the consumers andtake an action contemporaneously with the consumers being present atlocations, indicated by the location data, to exhibit consumer behaviorsof interest.

In one illustrative example of a way in which techniques describedherein may be used, a consumer analytics system may evaluate physicallocations through which a consumer passed and determine that theconsumer visited a particular retail store or interacted with some othercommercial entity that is a subject of consumer analysis. In response,the system may prompt the consumer to provide answers in response tosurvey questions, where the survey questions may include questionsrelating to the retail store visited by the consumer or otherinteraction with the commercial entity. In the example of a retailstore, the consumer analytics system may prompt the consumer to providethe answer contemporaneously with the consumer's visit to the retailstore, such as while the consumer is still visiting the retail store orhas just left the store. As another illustrative example, a consumeranalytics system may evaluate physical locations through which aconsumer passed and determine that the consumer visited a retail storethat the consumer does not normally visit, demonstrating a deviationfrom a pattern of behavior previously identified for the consumer. Inresponse to detecting the deviation, the consumer analytics system mayacquire social networking data for the consumer. The data may then bereviewed for data, contemporaneous with the consumer's deviation fromthe pattern. Such social networking data, when analyzed in combinationwith information about the behavior of deviating may indicate what mayhave caused the detected consumer behavior.

In embodiments in which an action to be taken by a consumer analyticssystem in response to determining characteristics, including behaviors,of consumers includes collecting information, the consumer analyticssystem may collect any suitable information. The information may, forexample, include information relating to commercial activity. Theinformation related to a commercial activity may include informationrelated, for example, to a commercial entity, a product, and/or aservice, including such commercial activity as an advertisement orproduct display. Information regarding commercial activity may includeinformation regarding commercial activity of a consumer, such ascommercial entities visited, products and/or services purchased, and/orpreferences of the consumer with respect to commercial entities,products, and/or services. Information regarding commercial activity mayadditionally or alternatively include information regarding commercialactivity of a consumer, which may include products and/or servicesoffered by the commercial entity, marketing efforts of the commercialentity, and/or monetary transactions in which the commercial entityengaged. A commercial entity may be any suitable entity that may engagein commerce with consumers, including an entity that may provideproducts and/or services directly to a consumer, such as a retailer, orthat may distribute products and/or services that may be purchased by aconsumer, such as a manufacturer, vendor, or wholesaler.

In embodiments in which a consumer analytics engine is configured tocollect information in response to determining characteristics ofconsumers, the consumer analytics system may be configured to collectinformation in any suitable manner. The consumer analytics system may beconfigured to collect information from a consumer and/or from one ormore data sources external to the consumer analytics system. Theconsumer analytics system may collect information from a consumer bysoliciting the information from the consumer. To solicit information,the consumer analytics system may send one or more messages to consumersdescribing the solicited information and/or describing tasks that theconsumers are requested to perform to obtain the solicited information.The task to be performed by the consumer may include interacting with acommercial entity, such as by visiting a commercial entity, visiting adepartment or section of a commercial entity, or speaking with staff ofthe commercial entity. The task may additionally or alternativelyinclude providing opinions or preferences of the consumer regardingcommercial activity. The consumer analytics system may collectinformation from one or more data sources external to the consumeranalytics system in any manner. External data sources may storeinformation regarding commercial activity and/or regarding one or moreconsumers, one or more commercial entities, or an environment of theconsumer(s) or the commercial entity(ies).

Information regarding a consumer may be related to a consumer in anysuitable way. The information regarding a consumer may includeinformation relating to an identity, behavior, or preferences of aconsumer, and/or information relating to commercial activity performedby or experienced by the consumer or with which the consumer interacted.In some cases, the information relating to a consumer may includeinformation created by a consumer. Information created by a consumer andstored in an external data store may include social networking datamaintained by a social networking service. Information regarding acommercial entity may be related to the commercial entity in anysuitable way. The information regarding the commercial entity may beinformation maintained by a commercial entity regarding commercialactivity of the commercial entity, including monetary transactionsengaged in by the commercial entity, information on loyalty programs ofthe commercial entity, and/or information on marketing information forthe commercial entity. Information regarding an environment of aconsumer and/or a commercial entity may include any suitableenvironmental data, including information regarding environmentalconditions that may impact commercial activities, such as by impactingthe likelihood of consumers and commercial entities engaging in monetarytransactions. For example, information regarding weather conditionsand/or information regarding gas prices may be collected by the consumeranalytics system.

A consumer analytics system operating according to techniques describedherein is not limited to taking actions that include collectinginformation. A consumer analytics system may, in response to determiningthat characteristics of consumers satisfy conditions for an action, takean action that includes storing information in one or more external datastores. To store information in an external data store, the consumeranalytics system may transmit the data to a computing device to bestored in the external data store. The consumer analytics system maystore any suitable information in an external data store, includinginformation regarding commercial activity, including commercial activityof a consumer and/or a commercial entity.

Thus, it should be appreciated that a consumer analytics systemoperating according to techniques described herein may take any suitableaction in response to determining, based at least in part on locationdata for consumers, that one or more characteristics of one or moreconsumers satisfy one or more conditions for the action. It should alsobe appreciated that the condition(s) for an action may relate to anysuitable characteristics of consumers, including characteristics thatmay be determined from evaluating location data for the consumers.Characteristics of consumers that may be determined from location datainclude behavior characteristics, identity characteristics, andpreference characteristics. Behavior characteristics may relate tobehaviors of consumers, including behaviors that relate to commercialactivities in which the consumers engage. Commercial activities of theconsumer for which behavior characteristics may be determined mayinclude activities in which a monetary transaction takes place or couldtake place. Behavior characteristics of a consumer may identify ordescribe an activity in any suitable way, including byidentifying/describing a nature of an activity, a frequency in which aconsumer engages in the activity, or a context in which the consumerengages in the activity. Further details regarding behaviorcharacteristics, identity characteristics, and preferencecharacteristics are provided below.

A consumer analytics system may take an action regarding a consumer(such as soliciting information from the consumer or acquiringinformation regarding commercial activity of the consumer) in responseto predicting and/or inferring one or more characteristics of thatconsumer, or in response to predicting and/or inferring one or morecharacteristics of any other consumer(s), including for one or moregroups of consumers. When an action is associated with multipleconditions that relate to multiple consumer characteristics, each of themultiple characteristics satisfying the conditions for the action may bepredicted and/or inferred at any suitable time relative to one another,including at the same time or at different times.

Additionally, the characteristic(s) of one or more consumers thatsatisfy the condition(s) for an action may be determinedcontemporaneously with the predicted and/or inferred presence of theconsumer(s) at locations identified by location data from which thecharacteristics were determined. The characteristics may be determinedcontemporaneously with the consumers' presence because thecharacteristics are determined while the consumers are still present atthe location, because the consumers are close to arriving at thelocation, or because the consumers have recently left the location. Insome embodiments, the characteristics may be determinedcontemporaneously with the predicted and/or inferred presence of theconsumers at the locations because the characteristics are determinedbefore the consumer exhibits different characteristics by moving to oneor more different locations from which a different characteristic ispredicted and/or inferred. For example, when a behavior of the consumersatisfying a condition of the action is predicted and/or inferred fromthe consumer's presence at a location, the characteristics may bedetermined before the consumer moves to a different location from whicha different behavior of the consumer is predicted and/or inferred. Whenmultiple different characteristics of consumers are predicted and/orinferred at different times to satisfy conditions for an action, any orall of the characteristics may be predicted and/or inferredcontemporaneously with the consumer's predicted and/or inferred presenceat the location. The consumer analytics system may also take the actionassociated with the condition(s) contemporaneously with the predictedand/or inferred presence of a consumer at a location from which thecharacteristic(s) satisfying the condition(s) for the action weredetermined. When multiple characteristics satisfying conditions for anaction are determined at different times, the action may be takencontemporaneously with a consumer's presence at a location from whichwas determined the characteristic of the consumer that was determinedlast in time.

Location data that may be analyzed by a consumer analytics system todetermine characteristics of consumers may be in any suitable form.Location data may be in a form electronically derived throughmeasurements of location performed by a portable computing device.Location data that is electronically-derived through measurement mayidentify a physical location of the consumer, including a geographiclocation of the consumer. Location data identifying a measured physicallocation of the consumer may be derived in any suitable manner,including using a satellite navigation system and/or triangulationtechnique. Additionally or alternatively, location data may includeinformation indicating a setting visited by a consumer, such asinformation collected from a consumer and/or information collected froman external data source. Information indicating a setting visited by aconsumer may include information provided by a consumer to a socialnetworking service, or any other suitable information. Thus, a consumeranalytics system operating according to techniques described herein maybe configured to take an action in response to determining that one ormore characteristics of one or more consumers satisfy one or moreconditions for the action to be taken.

For systems that have access to measured location data identifying acurrent location of consumers, behaviors, or other consumercharacteristics inferred from that location data may be correlated intime with other types of information by collecting that informationcontemporaneous with detecting the characteristic from analyzing thelocation data in near real time. However, in other system, that analysismay be performed on previously recorded data and other techniques may beused to synchronize other types of information with location-basedcharacteristics.

In some embodiments, as part of analyzing location data for consumers, aconsumer analytics system may perform a visit detection process. Thevisit detection process may identify, from location data for consumers,settings visited by the consumers. The visit detection process may beconfigured to recognize a set of settings with which the visit detectionprocess is configured, based on definitions for the settings. In someembodiments, a consumer analytics system may be configured to adjust thevisit detection process based on information collected by the consumeranalytics system. The information may have been collected by theconsumer analytics system when conditions for an action to collectinformation were met by characteristics of consumers. When informationis collected by the consumer analytics system, the information mayidentify a setting visited by a consumer. When the informationidentifies a setting visited by the consumer, the identified setting maybe used to adjust the visit detection process. For example, the settingmay be compared to a setting identified by the visit detection processfrom analyzing location data to determine whether there is a match. Whenthe settings match, the visit detection process may be adjusted toreflect that the visit detection process was correct, such as byadjusting a definition of the setting based on the location data thatwas correctly interpreted as corresponding to the setting. If, however,the settings do not match or if the visit detection process was unableto identify a setting from location data, the visit detection processmay be adjusted to attempt to improve the reliability of the visitdetection process and the likelihood that the visit detection processwill correctly interpret a location as corresponding to a setting.Adjusting the visit detection process may include adjusting a definitionof one or more settings, adjusting the set of settings, or adjustingparameters of the visit detection process.

Examples of ways in which techniques described herein may be implementedare described below. It should be appreciated, however, that embodimentsare not limited to operating in accordance with any of these examples.

Illustrative Context

FIG. 1 illustrates an exemplary environment in which some embodimentsmay operate to detect location data for one or more consumers and, byanalyzing that location data, determine characteristics of thoseconsumers. The example of FIG. 1 is described in connection with oneconsumer, but embodiments may operate with any number of consumers.

In the environment of FIG. 1, a consumer 102 changes location whilegoing to work, going home, going to school, running errands, or movingfrom any other place to place. In the specific example of FIG. 1, theconsumer 102 visits a golf course 121, cafe 122, and grocery store 123during a day. The consumer analytics system 108 monitors movements ofthe consumer 102 and, by detecting and analyzing locations the consumer102 visits, produces inferences and predictions regardingcharacteristics of the consumer, which may include inferences and/orpredictions of behavior characteristics relating to behaviors of theconsumer.

Embodiments may monitor movements of the consumer 102 in any suitablemanner. In some embodiments, location data for a consumer may becollected for the consumer using techniques described in U.S. patentapplication Ser. No. 12/910,280, filed on Oct. 22, 2010, and titled“Electronically capturing consumer location data for analyzing consumerbehavior” (“the '280 application”). The '280 application is incorporatedherein by reference in its entirety for all purposes and at least forits disclosure of collecting and analyzing location data for consumersto predict and/or infer characteristics of the consumers.

In some embodiments, the consumer 102 is associated with a device 104that can be used to obtain location information for the consumer 102 asthe consumer 102 moves. The consumer 102 may move with the device 104,as the consumer 102 may carry the device 104 or the device 104 may beembedded in a car, piece of clothing, or baggage carried by the consumer102. In some cases, the device 104 may be useful only in determining alocation of the consumer 102, while in other cases the device 104 mayhave additional functionality. For example, the device 104 may be amobile telephone with location-identifying capabilities, such as acellular telephone with a built-in Global Positioning System (GPS) orAssisted GPS (AGPS) receiver that the cellular telephone can use todetermine its current location. The device 104 may be able tocommunicate with a network 106, which may be any suitable communicationnetwork, including a wireless wide-area network (WWAN). In cases wherethe device 104 is a cellular telephone, the network 106 may be orinclude a cellular network.

The consumer analytics system 108 may obtain location data for aconsumer 102 from the device 104. In some cases, the consumer analyticssystem 108 may request the location information from the network 106and, in turn, the network 106 may obtain location data from the device104. In some embodiments, the consumer analytics system 108 may requestthe location data at varying intervals based on various factors,including the current location of the consumer 102.

The consumer analytics system 108 may analyze the location data toidentify settings visited by the consumer, including settings of the setof settings 109, and predict and/or infer characteristics of theconsumer 102. Inferring and/or predicting characteristics of theconsumer 102 may include inferring and/or predicting behaviors in whichthe consumer 102 engages, was engaging, or will engage. In someembodiments, when the system 108 infers and/or predicts one or morecharacteristics of one or more consumers (including the consumer 102),the characteristic(s) of the consumer(s) trigger the system 108 to takeone or more actions.

The system 108 may take any suitable action, as embodiments are notlimited to taking any particular action. In some embodiments, the actiontaken by the system 108 may include collecting information regardingcommercial activity, including commercial activity of consumers.Commercial activity of a consumer may include information regardingvisiting a commercial entity, purchasing a product or a service, and/orpreferences of the consumer regarding commercial entities, products,and/or services. Commercial entities, products, or services about whichinformation is obtained may be commercial entities, products, orservices to which an inferred or predicted characteristic of theconsumer 102 relates. For example, an inferred characteristic may relateto interactions of the consumer 102 with a commercial entity, such asbehaviors or preferences of the consumer 102 with respect to thecommercial entity. In such a case, the product or service about whichinformation is obtained may be a product or service of the commercialentity. In other cases, the commercial entity, product, or service maynot be related to an inferred or predicted characteristic, but may be aproduct or service for which market research is being conducted. Marketresearch may be conducted to determine characteristics of consumersrelated to the commercial entity, product, or service, and the marketresearch may include collecting information from or about consumers forwhich a characteristic has been inferred. When the characteristic isinferred for the consumer 102, then, the system 108 takes the action toobtain information about the product or service.

Embodiments are not limited to taking any particular action in responseto inferring or predicting any particular characteristic. As an exampleof an action that the system 108 may take, in some embodiments, inresponse to inferring and/or predicting behavior of the consumer 102,the system 108 may solicit information from the consumer regardingcommercial activity. To solicit the information, the system 108 may sendthe consumer 102 an alert or message on the device 104. The message sentto the device 104 may include a request for the consumer 102 to completea task. The task may include providing information to the system 108,which may include information regarding commercial activity. In somecases, the task included in the message may include answering surveyquestions provided to the consumer 102. The consumer 102 may, in someembodiments, respond to survey questions using the device 104. Examplesof other messages and tasks that may be provided to a consumer 102 bythe system 108 are described in greater detail below.

As another example of actions that may be taken by the system 108 inresponse to inferring or predicting one or more characteristics of oneor more consumers, the system 108 may acquire information from at leastone data source external to the system 108. The information acquiredfrom the at least one data source may be any suitable information, asembodiments are not limited in this respect. In some cases, theinformation may include information regarding the consumer 102,regarding an inferred characteristic, and/or regarding a commercialentity or a product or service offered by a commercial entity. Forexample, in response to inferring a characteristic of the consumer 102,the system 108 may obtain social networking data provided by a consumerto a social networking service or that relates to the consumer 102. Thesocial networking data may be evaluated to determine whether the socialnetworking data indicates information relating to the characteristicand/or to a product or service. For example, the social networking datamay include a review of a product or service indicating opinions of theconsumer 102 regarding the product or service. Examples of other typesof external data sources from which information may be obtained aredescribed in greater detail below.

Examples of ways in which a consumer analytics system may processlocation data for multiple consumers, determine characteristics ofconsumers, and take actions based on determined characteristics aredescribed in greater detail below. It should be appreciated that some ofthe examples below may not be described in connection with theillustrative environment described above in connection with FIG. 1.Embodiments are not limited to operating in any particular environment,including the environment of FIG. 1. Further, it should be appreciatedthat embodiments are not limited to acting in accordance with any of theexamples below. Embodiments may operate in any suitable manner toprocess location data for consumers related to movements of theconsumers in any suitable environment.

Illustrative Techniques

FIG. 2 illustrates one example of an overall process for collectingrelevant and timely data from and about consumers to make inferences andpredictions by using electronically-captured location data. The processof FIG. 2 begins in block 201, in which a set of actions to-be-triggeredare input. The actions may be specified by any suitable one or moreparties, as embodiments are not limited in this respect. In someembodiments, the actions may be specified by an administrator of aconsumer analytics system. In other embodiments, the actions mayadditionally or alternatively be specified by one or more marketresearchers as part of defining a market research study. In embodimentsin which the actions are specified as part of defining a study, theactions specified in block 201 may include actions to be taken by theconsumer analytics system to collect information to be analyzed as partof the study. Actions to collect information may include actions tosolicit information from one or more consumers and/or acquireinformation from one or more external data sources. Any suitable partymay act as a market researcher in these embodiments, includingprofessional market researchers or laymen doing market research.Additionally, the study may relate to any suitable topic. For example, amarket research study may be carried out to determine characteristics ofconsumers that relate to a setting, of the set of setting 109 of theenvironment of FIG. 1, based on information about consumers of interest.The setting of the set 109 may be a commercial entity, such as a retailbusiness.

Any suitable information regarding actions to be taken may be specifiedin block 201. In some embodiments, information describing the action tobe taken may be specified. For example, where the action includesrequesting that a consumer perform a task, the task may be described.Any suitable task to be performed by a consumer may be included in anaction, as embodiments are not limited in this respect. In some cases, atask may include prompting a consumer to answer survey questions, inwhich case the survey questions and, optionally, acceptable answers tothe questions may be specified in block 201. In other cases, a task mayinclude prompting a consumer to obtain media or scan a Universal ProductCode (UPC) barcode or Near Field Communication (NFC) tag, in which casethe subject of the desired media or the object desired to be scanned maybe specified in block 201. In still other cases, a task may includerequesting that a consumer visit a setting and provide information oropinions about the setting, such as providing opinions regarding anarrangement of items in a setting, and the setting and topic of thedesired opinion may be specified in block 201.

Additionally, specifying the action in block 201 may include specifyingone or more conditions that, when satisfied, will result in the consumeranalytics system taking the action. Any suitable conditions may bespecified, including conditions related to one or more characteristicsof one or more consumers determined from location data. For example, acondition may be satisfied when the consumer analytics systemdetermines, from location data for a consumer, a characteristic of aconsumer. A characteristics of a consumer may be a behaviorcharacteristic of a consumer relating to a behavior in which theconsumer was engaging when the location data was derived. Such acharacteristic may be, for example, that the consumer is a customer of acommercial entity. As another example of a condition, a condition may besatisfied when the consumer analytics system determines a characteristicof a group of consumers. A characteristic of a group of consumers may bea characteristic of the group and not of individual consumers of thegroup (e.g., an average characteristic for the group) or acharacteristic shared by consumers of the group. As another example of acondition, a condition may be satisfied based on an evaluation of acharacteristic that describes a behavior. For example, a behaviorcharacteristic may relate to a frequency with which a consumer performsa behavior, such as a frequency with which the consumer visits a retailbusiness. An example of a condition that may be associated with anaction is a condition that a behavior characteristic indicate that afrequency of a consumer's visits to a retail business is greater thantwo visits per month.

In one illustrative example of an action and a condition, an actionincludes requesting that a consumer respond to survey questionsregarding a commercial entity for which market research is beingconducted, and a condition for the action is that an analysis oflocation data for a consumer produces an inference that the consumer isa customer of the commercial entity. This action and condition may bespecified in block 201. Subsequently (as discussed below), when locationdata for a consumer is analyzed and a characteristic indicating that aconsumer is a customer of the commercial entity is inferred, theconsumer analytics system may prompt that consumer to provide responsesto the survey questions. The action taken by the consumer analyticssystem to prompt the consumer may be taken by the systemcontemporaneously with the consumer's presence at a location from whichthe characteristics satisfying the conditions were inferred. As anotherexample, a system may infer from location data that consumers of a groupof consumers who frequently shop at one store (or type of store) arevisiting a competitor store not frequently visited by consumers of thegroup. In response to drawing the inference, the system may surveyindividual consumers who are members of the group to determine a purposeof the consumers' visits to the competitor store. The surveying may beconducted electronically, by transmitting messages to the consumers, andmay be performed contemporaneously with the consumer's visit to thecompetitor store.

In block 202, location data is obtained for multiple consumers. Anysuitable location data may be obtained, as embodiments are not limitedin this respect. Location data may, in some embodiments, includegeographic location data identifying a geographic location that resultsfrom a location measurement performed by a computing device using alocation identification system like the Global Positioning System (GPS).A geographic location of a consumer may be defined according to alatitude, longitude, altitude, and/or margin of error that identifiesthe precision of the latitude, longitude, and altitude. Location datamay also include time data indicating a time at which the location datafor the consumer was obtained. Illustrative examples of location dataare discussed below.

The location data may be obtained in any suitable manner. Examples oflocation data that may be obtained and ways in which location data maybe obtained are discussed in detail below and in the '280 applicationthat is incorporated herein by reference. In some embodiments, thelocation data for a consumer may be obtained in part using an electronicdevice associated with a consumer. The electronic device may be anysuitable portable device that may move along with the consumer. Thedevice may be carried by the consumer or may be integrated into an itemassociated with the consumer (e.g., integrated into a car, baggage, orclothing). The electronic device may obtain location data or be used inobtaining location data. Location data obtained by the electronic devicemay be transmitted to a consumer analytics system at any suitable timeand in any suitable manner. In some embodiments, the electronic devicemay continuously or occasionally transmit location data for the consumerto a consumer analytics system without receiving a request for thelocation data from the system. In other embodiments, the consumeranalytics system may occasionally request location data from theelectronic device and the electronic device may transmit the locationdata upon receipt of the request. In still other embodiments, theelectronic device may transmit location data without request at sometimes and the consumer analytics system may request location data atother times.

In block 203, the location data for each consumer of the multipleconsumers is processed to determine characteristics for the consumers.As described in the '280 application that is incorporated herein byreference, the characteristics for a consumer that may be determinedfrom location data include behavior characteristics, preferencecharacteristics, and identity characteristics. In block 203, determiningthe characteristics of a consumer includes predicting and/or inferringbehavior characteristics of the consumer. The behaviors of a consumerthat may be indicated by characteristics may include visiting aparticular setting (e.g., a particular store), doing a specific activitysuch as playing golf, or traveling via a specific mode oftransportation. The processing of location data of block 203 may beperformed by the consumer analytics system contemporaneously with theconsumer's movements, as the location data is obtained for the consumer,such as while the consumer is visiting a setting or moving to one ormore settings on a path.

As part of the processing of location data for the consumers, theconsumer analytics system may determine whether to take an action,including whether to request that the consumer perform a task. Todetermine whether to take an action, characteristics of consumersinferred and/or predicted during the processing of block 203 arecompared to conditions for actions specified in block 201. Whenconditions for an action are satisfied, the consumer analytics systemmay take the action. Accordingly, in block 204, based on thecharacteristics of the consumer inferred or predicted in block 203, anaction is triggered when the characteristics satisfy one or moreconditions. As discussed above, any suitable actions may have beenspecified in block 201 and may be taken in block 204. Actions mayinclude sending a consumer one or more survey questions to respond to.The actions may additionally or alternatively include obtainingadditional data from an external data source, such as data related tothe consumer. Data related to the consumer may include sales transactiondata, information entered into social networking or other system, or anyother information. As another example, actions may include adjusting oneor more parameters of a visit detection process. The action taken by theconsumer analytics system may be taken at any suitable time, includingcontemporaneously with the consumer's movements.

Overview of Illustrative Computing System

Some embodiments include a consumer analytics system, implemented on acomputing device, with a configured set of actions. The consumeranalytics system may include a facility for processing location data, aset of points of interest, and a set of actions which can be performed.The facility may be executed by the computing device.

Techniques operating according to principles described herein may beimplemented in any suitable manner. For example, the methods and systemsdescribed herein may be deployed in part or in whole through a machinethat executes computer software, program codes, and/or instructions on aprocessor. The processor may be part of a server, client, networkinfrastructure, mobile computing platform, stationary computingplatform, or other computing platform. A processor may be any kind ofcomputational or processing device capable of executing programinstructions, codes, binary instructions and the like. The processor maybe or include a signal processor, digital processor, embedded processor,microprocessor or any variant such as a co-processor (math co-processor,graphic co-processor, communication co-processor and the like) and thelike that may directly or indirectly facilitate execution of programcode or program instructions stored thereon. In addition, the processormay enable execution of multiple programs, threads, and codes. Thethreads may be executed simultaneously to enhance the performance of theprocessor and to facilitate simultaneous operations of the application.By way of example, methods, program codes, program instructions and thelike described herein may be implemented in one or more threads. Thethreads may spawn other threads that may have assigned prioritiesassociated with them; the processor may execute these threads based onpriority or any other order based on instructions provided in theprogram code. The processor may include memory that stores methods,codes, instructions and programs as described herein and elsewhere. Theprocessor may access a storage medium through an interface that maystore methods, codes, and instructions as described herein andelsewhere. The storage medium associated with the processor for storingmethods, programs, codes, program instructions or other type ofinstructions capable of being executed by the computing or processingdevice may include but may not be limited to one or more of a CD-ROM,DVD, memory, hard disk, flash drive, RAM, ROM, cache and the like.“Storage medium,” as used herein, refers to tangible storage media.Tangible storage media are non-transitory and have at least onephysical, structural component. In a storage medium, at least onephysical, structural component has at least one physical property thatmay be altered in some way during a process of creating the medium withembedded information, a process of recording information thereon, or anyother process of encoding the medium with information. For example, amagnetization state of a portion of a physical structure of acomputer-readable medium may be altered during a recording process.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. A software program may be associated with a serverthat may include a file server, print server, domain server, internetserver, intranet server and other variants such as secondary server,host server, distributed server and the like. The server may include oneor more memories, processors, storage media, ports (physical andvirtual), communication devices, and/or interfaces capable of accessingother servers, clients, machines, and devices through a wired or awireless medium, and the like. The methods, programs or codes asdescribed herein and elsewhere may be executed by the server. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers andthe like. Additionally, this coupling and/or connection may facilitateremote execution of programs across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe invention. In addition, any of the devices attached to the serverthrough an interface may include at least one storage medium capable ofstoring methods, programs, code and/or instructions. A centralrepository may provide program instructions to be executed on differentdevices. In this implementation, the remote repository may act as astorage medium for program code, instructions, and programs.

A software program may be associated with a client that may include afile client, print client, domain client, internet client, intranetclient and other variants such as secondary client, host client,distributed client and the like. The client may include one or morememories, processors, storage media, ports (physical and virtual),communication devices, and interfaces capable of accessing otherclients, servers, machines, and devices through a wired or a wirelessmedium, and the like. The methods, programs or codes as described hereinand elsewhere may be executed by the client. In addition, other devicesrequired for execution of methods as described in this application maybe considered as a part of the infrastructure associated with theclient.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers andthe like. Additionally, this coupling and/or connection may facilitateremote execution of programs across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe invention. In addition, any of the devices attached to the clientthrough an interface may include at least one storage medium capable ofstoring methods, programs, applications, code and/or instructions. Acentral repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for program code, instructions, and programs.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM and the like. The processes, methods, program codes, andinstructions described herein may be executed by one or more of thenetwork infrastructural elements.

The methods, program codes, and instructions described herein andelsewhere may be implemented on a cellular network having multiplecells. The cellular network may either be frequency division multipleaccess (FDMA) network, a time division multiple access (TDMA) network,and/or a code division multiple access (CDMA) network, or any othersuitable form of network implementing any suitable communicationprotocol and any suitable medium access control protocol. The cellularnetwork may include mobile devices, cell sites, base stations,repeaters, antennas, towers, and the like. The cell network may be anetwork carrying out a protocol for Global System for MobileCommunications (GSM), General Packet Radio Service (GPRS), anythird-generation (3G) network, Evolution-Data Optimized (EVDO), ad hocmesh, Long-Term Evolution (LTE), Worldwide Interoperability forMicrowave Access (WiMAX), or other network types.

The methods, programs codes, and instructions described herein andelsewhere may be implemented on or through mobile devices. The mobiledevices may include navigation devices, cell phones, mobile phones,mobile personal digital assistants, laptops, palmtops, netbooks, pagers,electronic books readers, music players and the like. These devices mayinclude, apart from other components, a storage medium such as a flashmemory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled toexecute program codes, methods, and instructions stored thereon.Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute program codes. The mobile devices may communicate on a peer topeer network, mesh network, or other communications network. The programcode may be stored on the storage medium associated with the server andexecuted by a computing device embedded within the server. The basestation may include a computing device and a storage medium. The storagedevice may store program codes and instructions executed by thecomputing devices associated with the base station.

Computer software, program codes, and/or instructions may be storedand/or accessed on machine readable storage media that may include:computer components, devices, and recording media that retain digitaldata used for computing for some interval of time; semiconductor storageknown as random access memory (RAM); mass storage typically for morepermanent storage, such as optical discs, forms of magnetic storage likehard disks, tapes, drums, cards and other types; processor registers,cache memory, volatile memory, non-volatile memory; optical storage suchas CD, DVD; removable media such as flash memory (e.g. USB sticks orkeys), floppy disks, magnetic tape, paper tape, punch cards, standaloneRAM disks, Zip drives, removable mass storage, off-line, and the like;or other computer memory such as dynamic memory, static memory,read/write storage, mutable storage, read only, random access,sequential access, location addressable, file addressable, contentaddressable, network attached storage, storage area network, bar codes,magnetic ink, and the like.

The methods and systems described herein may transform physical and/oror intangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The elements described and depicted herein, including in flow charts andblock diagrams throughout the figures, imply logical boundaries betweenthe elements. However, according to software or hardware engineeringpractices, the depicted elements and the functions thereof may beimplemented on machines through computer executable media having aprocessor capable of executing program instructions stored thereon as amonolithic software structure, as standalone software modules, or asmodules that employ external routines, code, services, and so forth, orany combination of these, and all such implementations may be within thescope of the present disclosure. Examples of such machines may include,but may not be limited to, personal digital assistants, laptops,personal computers, mobile phones, other handheld computing devices,medical equipment, wired or wireless communication devices, transducers,chips, calculators, satellites, tablet PCs, electronic books, gadgets,electronic devices, devices having artificial intelligence, computingdevices, networking equipments, servers, routers and the like.Furthermore, the elements depicted in the flow chart and block diagramsor any other logical component may be implemented on a machine capableof executing program instructions. Thus, while the drawings anddescriptions herein set forth functional aspects of the disclosedsystems, no particular arrangement of software for implementing thesefunctional aspects should be inferred from these descriptions unlessexplicitly stated or otherwise clear from the context. Similarly, itwill be appreciated that the various steps identified and describedherein may be varied, and that the order of steps may be adapted toparticular applications of the techniques disclosed herein. All suchvariations and modifications are intended to fall within the scope ofthis disclosure. As such, the depiction and/or description of an orderfor various steps should not be understood to require a particular orderof execution for those steps, unless required by a particularapplication, or explicitly stated or otherwise clear from the context.

The methods and/or processes described herein, and steps thereof, may berealized in hardware, software or any combination of hardware andsoftware suitable for a particular application. The hardware may includea general purpose computer and/or dedicated computing device or specificcomputing device or particular aspect or component of a specificcomputing device. The processes may be realized in one or moremicroprocessors, microcontrollers, embedded microcontrollers,programmable digital signal processors or other programmable device,along with internal and/or external memory. The processes may also, orinstead, be embodied in an application specific integrated circuit, aprogrammable gate array, programmable array logic, or any other deviceor combination of devices that may be configured to process electronicsignals. It will further be appreciated that one or more of theprocesses may be realized as a computer executable code capable of beingstored on a machine readable medium.

Computer executable code may be created using a structured programminglanguage such as C, an object oriented programming language such as C++,or any other high-level or low-level programming language (includingassembly languages, hardware description languages, and databaseprogramming languages and technologies) that may be stored, compiled orinterpreted to run on one of the above devices, as well as heterogeneouscombinations of processors, processor architectures, or combinations ofdifferent hardware and software, or any other machine capable ofexecuting program instructions.

Thus, in one aspect, each method described herein and combinationsthereof may be embodied in computer executable code that, when executingon one or more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, the means for performingthe steps associated with the processes described herein may include anyof the hardware and/or software described herein. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

FIG. 3 illustrates one exemplary implementation of a computing device inthe form of a computing device 300 that may be used in a systemimplementing the techniques described herein, although others arepossible. It should be appreciated that FIG. 3 is intended neither to bea depiction of necessary components for a computing device to operate inaccordance with the principles described herein, nor a comprehensivedepiction.

Computing device 300 may comprise at least one processor 302, a networkadapter 304, and computer-readable storage media 306. Computing device300 may be, for example, a desktop or laptop personal computer, aserver, a collection of personal computers or servers that operatetogether, or any other suitable computing device. Network adapter 304may be any suitable hardware and/or software to enable the computingdevice 300 to communicate wired and/or wirelessly with any othersuitable computing device over any suitable computing network. Thecomputing network may include wireless access points, switches, routers,gateways, and/or other networking equipment as well as any suitablewired and/or wireless communication medium or media for exchanging databetween two or more computers, including the Internet. Computer-readablemedia 306 may be adapted to store data to be processed and/orinstructions to be executed by processor 302. Processor 302 enablesprocessing of data and execution of instructions. The data andinstructions may be stored on the computer-readable storage media 306and may, for example, enable communication between components of thecomputing device 300.

The data and instructions stored on computer-readable storage media 306may comprise computer-executable instructions implementing techniquesthat operate according to the principles described herein. In theexample of FIG. 3, computer-readable storage media 306 storescomputer-executable instructions implementing various facilities andstoring various information as described herein. Computer-readablestorage media 306 may store a location data processing facility 309 forobtaining location data for consumers via network adapter 304 anddetermining characteristics, including behaviors, of the consumers. Thelocation data processing facility 309 may perform any of the exemplarytechniques described herein, and may include any of the exemplaryfacilities described herein. Computer-readable storage media 306 mayalso include data sets to be used by the location data processingfacility 309, including a data set 308 of actions to run and theirassociated triggering values, and a data set 310 of points of interests,which could include information about locations and types of points ofinterest.

While not illustrated in FIG. 3, a computing device may additionallyhave one or more components and peripherals, including input and outputdevices. These devices can be used, among other things, to present auser interface. Examples of output devices that can be used to provide auser interface include printers or display screens for visualpresentation of output and speakers or other sound generating devicesfor audible presentation of output. Examples of input devices that canbe used for a user interface include keyboards, and pointing devices,such as mice, touch pads, and digitizing tablets. As another example, acomputing device may receive input information through speechrecognition or in other audible format.

While the invention has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present invention isnot to be limited by the examples herein, but is to be understood in thebroadest sense allowable by law.

Examples of Techniques for Obtaining Location Data

As mentioned above, embodiments are not limited to implementing anyparticular technique for obtaining location data. In some embodiments,techniques for obtaining location data described in the '280 applicationincorporated herein by reference may be implemented.

In some embodiments, a system may use one or more of many differentmethods for gathering consumer location data based on a personal device(such as a mobile phone, tablet, or laptop computer). Location data mayinclude information identifying a geographic location. Informationidentifying a geographic location may include latitude, longitude,altitude, and an error measure. Location data may also include atimestamp. In some embodiments, an electronic device associated withand/or operated by a consumer may determine the location data alone andtransmit the determined location data to a consumer analytics system. Inothers, one or more other devices, such as components of a network towhich the electronic device is connected and/or able to communicate, maycooperate with the electronic device to determine the location data.

Techniques for obtaining location data that may be used in embodimentsinclude techniques for measuring a physical location of a consumer.Techniques for measuring a location including cell tower identification,enhanced cell identification, Uplink-Time difference of arrival, Time ofarrival, Angle of arrival, enhanced observed time difference (E-OTD),GPS, Assisted-GPS, hybrid positioning systems, Global NavigationSatellite System (GLONASS), the Galileo navigation system,location-determination services using access points for wireless localarea networks (WLANs), and the like.

In some embodiments, location data comprising measurements of physicallocation may additionally or alternatively be obtained using paging,triangulation, and the like. A common method is to triangulate alocation of the device based on nearby towers that provide wirelessphone/data service. In the case of mobile phones, the phones may emit aroaming signal to contact the next nearby antenna tower. The phone'sposition can be figured out by multilateration based on the signalstrength of nearby antennas. A similar method is to do a similartriangulation but instead of using towers used to provide wirelessservice, use Wi-Fi or other similar systems. This may be particularlyuseful in cases in which mobile tower signal is poor (in remote areas,for example) or not available on the device.

In some embodiments, in addition to or as an alternative to obtaininglocation data that includes measurements of physical location usingsatellite-based systems and/or triangulation, location data may bedetermined from information stored by data sources that are linked tothe user and/or device. Such data may include identifications by a userof setting visited by the consumer or that the consumer is visiting. Forexample, if a consumer provides information to a data source indicatinga location of the consumer, that information may be used in identifyinga location of the consumer. Such information may include a messageposted to a social networking service saying “I just arrived in Boston.”From the user's statement of his or her location, a consumer analyticssystem with access to the information can infer that the consumer is inthe vicinity of Boston. Additionally or alternatively, predictions oflocation may be used. Predictions may be obtained in any suitablemanner. For example, by using an accelerometer built into an electronicdevice that is carried by a consumer (e.g., an accelerometer of a mobilephone), a speed the consumer is traveling may be estimated and usedalong with a last known location for the consumer to estimate a currentlocation of the consumer. In some embodiments, multiple different kindsof data indicative of location may be analyzed together in determininglocations visited by consumers, which may increase the amount andquality of location data.

In some embodiments, different data sources may also be used to increasethe quality of the data collected by changing which data sources areused and how often the data sources are polled. For example, if locationdata indicates a consumer is moving, it may be useful to increase therate at which data is gathered.

Location data for consumers may be obtained by a consumer analyticssystem in any suitable manner. In some embodiments, location data can bepulled by the system. To pull the location data, the consumer analyticssystem may query a communication network, such as a communicationnetwork to which an electronic device associated with a consumer isconnected. The network may locate the device in response to the queryand produce location data and/or request that the device providelocation data. In other embodiments, the consumer analytics system mayobtain location data for a consumer by having an electronic deviceassociated with the consumer push location data to the consumeranalytics system periodically. In some embodiments in which a devicepushes location data periodically, it may be desirable that the deviceobtains location data and sends the location data to the systemautomatically and transparently to a consumer associated with thedevice, without receiving input from the user.

Examples of Processing Location Data to Build a Consumer Profile

The consumer analytics system may receive multiple different units oflocation data for any given consumer over time. The location data for aconsumer may be in the form of a set of data points that each identify alocation through which the consumer passed.

From analyzing this location data, a consumer analytics system maygenerate a unique list of settings visited by each consumer. The listmay be “unique” in that the list does not include multiple entriescorresponding to a single visit by a consumer to a setting, or becausethe list does not include multiple listings for a setting. To generatethe unique list, the consumer analytics system may identify “anchors”from locations that are similar in time and space. The consumeranalytics system may also identify settings corresponding to the anchorsand may produce information about a consumer based on the settingsvisited by a consumer. Additionally, by analyzing the unique list ofphysical locations and/or settings visited by a consumer, patterns canbe identified in the settings that may be used by the consumer analyticssystem to determine characteristics of a consumer. For example, anidentity, behaviors, and preferences of the consumer can be identifiedthrough analysis of location data. The location data that is analyzedmay include an identification of locations at which the consumer waspresent and/or settings visited by the consumer. Additionally,personally-relevant locations for the consumer, such as the place ofresidence and place of employment of the consumer, can be determinedthrough analysis.

The consumer analytics system may also examine sets of location pointscorresponding to movement, rather than only location pointscorresponding to stops the consumer made at particular locations, todetermine characteristics of a consumer. Location data corresponding tomovement may provide information about paths traveled by a consumer. Forexample, by using the distance and time between points, the consumer'sspeed can be computed. The consumer's speed, along with whether or notthe points are over roads, rail lines, etc. may be used to determine ifa consumer is traveling by car, rail, plane, etc. In addition, thedistance from the consumer's home of a location visited by a consumercan be computed using information about a path.

The '280 application that is incorporated herein by reference describesin detail techniques that may be implemented in some embodiments fordetermining anchors, paths, and settings from location data for aconsumer. The '280 application also describes in detail techniques thatmay be implemented in some embodiments for analyzing location data,anchors, paths, and settings to determine characteristics of consumer.

Visit Detection

In some embodiments, when a consumer analytics system receives locationdata for a consumer, the consumer analytics system may perform a visitdetection process on the location data to identify settings visited byconsumers. A setting may be a place to which a location corresponds,such as a commercial or non-commercial place (e.g., business or park). Aposition of a setting may correspond to a set of physical locationfalling within defined location boundaries of the setting, as discussedbelow. When a consumer is detected to have been present at a locationfalling within the location boundaries of a setting, through a visitdetection process the consumer can be detected to have visited thesetting.

A visit detection process may be performed by a consumer analyticssystem in any suitable manner, as embodiments are not limited toidentifying settings visited by consumers in any particular way.Examples of ways in which a visit detection process may be carried outare described below and in the '280 application that is incorporated byreference herein.

A visit detection process may be carried out because, in someembodiments, one element of detecting consumer characteristics,including behavior characteristics, from location data is to determinewhat stores, restaurants, sports venues, and other settings a consumervisits. The process 400 of FIG. 4 is an example of a visit detectionprocess that may be carried out in some embodiments.

The process 400 of FIG. 4 begins in block 402, in which a set oflocation data points for a consumer is obtained. The location datapoints may be obtained in any suitable manner, examples of which aredescribed above. In block 404, the location data may be analyzed toremove “noise” from the location data points. Noise in the location datapoints may include location data points that are not valid. Invalidlocation data points may include points indicating locations that arenot physically possible or very unlikely. Impossible or unlikelylocation data points may include data points such as:

-   -   Points that indicate the consumer is traveling faster than the        speed of sound; and    -   A trail of connected points roughly following a line with one        outlier that is clearly disconnected.

In order to remove the noise in block 404, the consumer analytics systemcan traverse the location data points for a consumer one-by-one anddiscard any location data points that do not meet one or more criteriafor not being noise or satisfy one or more criteria for being noise.Criteria for being noise may include detecting that a location datapoint is either physically impossible or very unlikely, or any othersuitable criteria.

Once noise is removed in block 404, as part of the processing oflocation data, in block 406 the consumer analytics system may enhancethe data by adjusting locations indicated by location data. For example,location data points may be pushed from unlikely places to likelyplaces. As an example, if the time and distance between points andaltitude indicate the consumer is likely traveling in a car, the pointsobtained during this time could be cross-referenced with the knownlocation of roads. The points could be moved to correspond to a road,which is most likely where the point is given that the car would likelybe driving on roads. Adjusting the location data points in this way maycompensate for errors in the locations identified by location datapoints, such as errors that may result from imprecise processes forobtaining location data.

Once a good set of location data points for a consumer have beenobtained through processing of block 402-406, the location data pointscan be analyzed to identify travel paths (“paths”) and stationarylocations (“anchors”). Paths and anchors may be identified by theconsumer analytics system in block 408 by looking at the time anddistance between points and by applying a clustering algorithm. Forexample, such a clustering of the sequential location points may becarried out using Euclidian distance clustering. In one example of aEuclidean distance clustering, locations within 200 meters of oneanother may be identified as being related to a same potential anchor.In some embodiments, each location identified by location data processedby the consumer analytics system may include an uncertainty radius. Theuncertainty radius around each location may be used to more accuratelycluster nearby location points using statistical methods. When alocation indicated by a location data point is similar to a locationindicated by another location data point and is within the uncertaintyradius of the other location data point, the consumer analytics systemmay conclude that the location data points both relate to one locationvisited by a consumer. An anchor may be identified at least in part as acluster of locations corresponding to multiple different location datapoints. Additionally, by comparing time differences between locationpoints related to the same potential anchor, a duration of time spent byconsumer at the potential anchor can be determined. Each cluster oflocations associated with a duration above a threshold, such as durationof greater than five minutes, may be identified by the consumeranalytics system as an anchor. In some embodiments, the calculatedlocation for an anchor may be a geometric mean of the individuallocation data points associated with the anchor.

In block 410, the consumer analytics system may use the anchors toidentify settings visited by a consumer. The consumer analytics systemmay utilize a data set of settings, including Points of Interest (POIs),to identify settings, including identifying locations corresponding toPOIs defined in the data set. The data set may include a collection ofplaces of one or more kinds (e.g., stores, restaurants, sports venues,transportation terminals, office buildings, etc.) that a consumer mayvisit. Each setting in the data set may be defined at least in part as apolygon that defines a location of the point of interest. Examples ofways in which the polygon may be defined are described in detail below.Additionally, in some embodiments, information regarding a setting mayinclude a set of operational information (e.g., the hours of operation,the operational type, e.g., a terminal for plane/boat/rail travel, etc.)and a set of categorical information about the setting (e.g., a retaillocation, restaurant, or stadium).

The consumer analytics system may identify the settings visited by theconsumer by examining each anchor and determining a likelihood that theconsumer visited the given POI. A consumer analytics system maydetermine the likelihood in any suitable manner, as embodiments are notlimited in this respect. In some embodiments, the likelihood may becalculated by the consumer analytics system based on a number offactors, including:

-   -   the likelihood that a cluster of location points representing        the anchor corresponds to a location within the bounds of the        POI;    -   whether the time range of the anchor falls within the        operational hours of the POI;    -   whether the anchor duration falls with the expected visit        duration to the given POI (e.g., consumers typically spend 1.5-3        hours at movie theatre; a visit of 30 minutes is unlikely);    -   whether the already-computed behavior of the consumer indicates        that she is likely to visit the POI or visit the POI at the        time-of-day, day-of-week, time-of-year, etc. at which location        data for the anchor was collected;    -   and any other suitable factors.

When a likelihood of an anchor matching a setting is calculated by theconsumer analytics system, the likelihood may be compared to athreshold. If the likelihood exceeds the threshold, the anchor may bedetermined to correspond to the setting and the consumer may bedetermined to have visited the setting. Any suitable threshold havingany suitable value may be used, as embodiments are not limited in thisrespect. Additionally, the threshold may be used for any suitable numberof settings. In some embodiments, the same threshold may be used by theconsumer analytics system for all settings, such that each time theconsumer analytics system calculates a likelihood of an anchorcorresponding to a setting, the likelihood may be compared to thethreshold. In other embodiments, different thresholds may be used fordifferent settings. In some embodiments that use multiple differentthresholds, each setting in the set of settings that can be identifiedthrough the visit detection process may be associated with an individualthreshold corresponding to that setting. When a likelihood of a consumervisiting the setting is calculated, the likelihood may be compared tothe threshold for that setting. In other embodiments that use multipledifferent thresholds, a group of multiple settings may share athreshold. Any suitable group of settings may be defined, as embodimentsare not limited in this respect. Settings having a similar location orbeing of a similar type may be grouped in some embodiments.

In block 412, once the consumer analytics system has matched locationdata for consumers to settings visited by the consumers in block 410,the consumer analytics system may store information resulting from thedetermination of block 410. The stored information may includeinformation identifying that a consumer has visited a setting, when ananchor for a consumer was determined to match a setting. The storedinformation may also include information identifying that an anchor of aconsumer was not matched to any settings, if the consumer analyticssystem could not match an anchor to settings. Once the information isstored in block 412, the process 400 ends.

Following the process 400, the information stored by the consumeranalytics system may be used in any suitable manner. For example, asdiscussed herein and in the '280 application incorporated herein byreference, settings visited by consumers may be analyzed to determinecharacteristics of consumers and/or to conduct market research.Characteristics of consumers determined from the settings may also becompared to conditions for actions, and a consumer analytics system maytake an action in response to determining that one or morecharacteristics of one or more consumers satisfy conditions for anaction. As another example, information identifying that an anchor for aconsumer does not match any settings for which the consumer analyticssystem has information may prompt adjustments to the visit detectionprocess, including adjustments to definitions of settings. As discussedin detail below, in some cases in which the consumer analytics systemcannot match an anchor for a consumer to a setting, the consumer and/oran administrator of the consumer analytics system may be prompted toprovide information about the location visited by the consumer and thisinformation may be used to define a setting. Once the setting isdefined, the consumer analytics system may be able to match anchors tothat setting.

The exemplary visit detection process described above in connection withFIG. 4 was described as being carried out by a consumer analytics systemin response to receiving location data from a source of location data,such as a device associated with a consumer. It should be appreciated,however, that embodiments are not limited to implementing the visitdetection process on a server or any other computing device thatreceives location data from another device. In some embodiments, adevice that measures a physical location of a consumer may perform avisit detection process. In such cases, the device may measure thephysical location of the consumer over time and apply a visit detectionprocess as above by comparing locations of the consumer to definitionsof settings. The set of settings may be stored on the device thatmeasures the location and performs the visit detection process or may bestored elsewhere accessible to the device, such as on a server that thedevice may communicate with over a network (e.g., a local network or awide-area network such as the Internet).

POI Data Set

As mentioned above, in some embodiments, POIs within the POI data setmay be defined in the data set using a polygon. As part of defining apolygon for a POI, the POI may be assigned “rooftop” latitudes andlongitudes that correspond to the boundaries of the POI, which maycorrespond to a building's physical footprint in the case where the POIis associated with building and boundaries of the POI correspond toboundaries of the building. While the boundaries may be described interms of a “rooftop,” it should be appreciated that POIs are not limitedto settings associated with buildings, and that some POIs may not have arooftop to which the boundaries of a polygon correspond. In the case ofa park, for example, a “rooftop” of the park may correspond to edges ofthe park.

Polygons defined in part based on rooftop latitude and longitudes mayassist in identifying settings visited by a consumer based on locationdata for the consumer. Many conventional POI data sets include onlyapproximate street-level location (which may be a nearest streetaddress) and may contain no information about the size of a POI.However, the street-level location for a POI may be hundreds of metersfrom a building's actual location. For example, many retailers, hotelsand sports venues are set back and separated from the street addressmarker by large parking lots. Thus, when location data for a consumerindicates the consumer's geographic location and the consumer visitssuch a setting, the consumer's geographic location may be hundreds ofmeters from the street-level location for the setting. Matching theconsumer's geographic location to a street-level location for a POI maytherefore be difficult for some POIs. Embodiments may thereforeimplement methods to populate a POI data set with rooftop data andproduce, based on the rooftop data, a polygon defining locations ofboundaries of a POI, including geographic locations of the boundaries.

Rooftop data and polygons may be provided to a POI data set of aconsumer analytics system in any suitable manner, as embodiments are notlimited in this respect. In some embodiments, a consumer analyticssystem generates preliminary information identifying locations of POIsusing commercially-available geo-location mapping services. A servicesuch as the Mapquest API Service or the SimpleGeo “Places” API may beused. The preliminary information identifying locations may be refinedto rooftop data. Any suitable technique for identifying rooftop data maybe used, including the two following examples.

Image Processing Boundary Detection:

This method identifies building boundaries in publicly availablesatellite images, such as those provided by Mapquest. Once a preliminarygeo-coded latitude/longitude is identified for a POI, a satellite imageis acquired which is centered on the geo-coded latitude/longitude, andan automated boundary detection scheme identifies all unique buildingrooftops in the image. Each building rooftop is scored against therooftop characteristics of the POI of interest, including but notlimited to, the shape (e.g. square, rectangular, ovoid), size/area, andproximity to other building rooftops. The building rooftop with thehighest score is matched to the POI of interest, and the confidence ofthe match is determined, taking into consideration the match scores toall nearby unique building rooftops. For example, to automaticallyidentify COSTCO® POIs, the scoring routine may indicate an optimal matchfor rectangular buildings with an area between a predefined range thatroughly corresponds to the range of expected COSTCO® building areas, andwhich are physically separated from nearby buildings.

The automated boundary detection scheme may be implemented in a varietyof ways, including, but not limited to, watershed edge detection, snakemodels, active contours (S. Ahmadi, Automatic urban building boundaryextraction from high resolution aerial images using an innovative modelof active contours, Int. J. Applied Earth Observation and Geoinformation12(3) 150-57 (2010), incorporated herein by reference in its entirety),and curve evolution (K. Karantzalos, Automatic model-based buildingdetection from single panchromatic high resolution images, Proceedingsof the Int. Society for Photogrammetry and Remote Sensing CongressVolume XXXVII Part 3A, Pages 127-32 (2008), incorporated herein byreference in its entirety). In one implementation, the satellite imageis first converted to greyscale, histogram equalized, Wiener filtered toremove high-frequency noise, converted to black/white, filtered toremove white segments less than a minimum specified area, opened andclosed to remove edge noise, and holes filled, prior to watershedboundary detection. In a final step, a best-fit rectangle can be matchedto each building boundary.

The robustness of this automated boundary detection approach can beimproved by manually verifying (and adjusting, if necessary), theboundaries of POIs whose confidence is less than a minimum confidencethreshold, e.g. through manual boundary adjustment.

Efficient Manual Boundary Detection & Adjustment:

This method utilizes a graphical software interface, e.g. via aweb-based application, so that individuals can efficiently andaccurately verify POI locations on a satellite map and, if needed, clickon the map to adjust the POI location. In this graphical application,the preliminary geo-coded location of each POI is displayed on asatellite map, and the user can re-center the marker onto the rooftop ofthe POI simply by clicking on the POI. The user can next identify theboundaries of the POI by clicking on all corners of the building.Alternately, the boundaries of the POI can be efficiently estimated by asingle click which specifies the radius of a best-fit circle with centerpoint set by the first mouse click. The user may repeat this processmultiple times until satisfied with the boundary placement, and then maysave the final boundary choice (e.g. by clicking a ‘save’ button), atwhich time the POI boundaries are saved to the POI database. The primaryadvantage of this method is that the user may consult multiple forms ofimagery, e.g. 45 degree or birds-eye-view aerial imagery, and streetview imagery, in addition to orthogonal satellite imagery to verify theidentity of the POI of interest prior to recording its boundaries. Thismethod may be used in combination with the automated boundary detectionmethod to verify/adjust automatically determined POI boundaries, whennecessary.

Triggering Data Collection Actions Based on Determined ConsumerCharacteristics

In some embodiments, location data, as well as settings visited by aconsumer and/or paths or trips taken by consumers, may be analyzed bythe consumer analytics system to infer and/or predict characteristics ofconsumers or groups of consumers. The characteristics of consumers maybe used to build profile about consumers, and these profiles may be usedto perform market research. In addition, in some embodiments, locationdata can be used to discover when a consumer exhibits characteristics ofinterest, including performing a behavior of interest.

A characteristic of interest, including a behavior of interest, may beany suitable characteristic (including a behavior characteristic) of aconsumer that may be determined from location data and in which a marketresearcher may be interested. Characteristics of interest, as mentionedabove, may be related to conditions of an action that may be taken by aconsumer analytics system. The characteristics may relate to commercialactivities of consumers. For example, a market researcher may beinterested in better understanding how consumers choose which kind ofpeanut butter to buy. By processing consumers' location data andidentifying, using a visit detection process, stores visited byconsumers, the consumer analytics system may be able to detect when aconsumer has arrived at or was present at a store that sells peanutbutter. In response to inferring a behavior characteristic for aconsumer indicating that the consumer has visited the store, theconsumer analytics system may take an action that includes sending theconsumer a message prompting the consumer to answer survey questions.The survey questions may ask whether the consumer bought peanut butter,which, if any, kinds of peanut butter the consumer bought, and why,and/or kinds of peanut butter the consumer did not buy and why not. Theconsumer's responses to these survey questions may aid the marketresearcher in understanding the mindset that went into the consumer'sdecision to purchase peanut butter.

As mentioned above, characteristics of a consumer that may be determinedfrom location data include behavior characteristics of consumers thatrelate to behaviors of the consumers. Behaviors of consumers may includebehaviors that extend for a period of time. For example, a consumer'svisit to a setting or a consumer's shopping trip that includes visitingone setting and driving past another setting may be behaviors thatextend for a period of time (e.g., the period of time the consumer wasat a setting). When a behavior extends for a period of time, in someembodiments a consumer analytics system may obtain location data for theconsumer, determine characteristics for the consumer, and carry out anaction while the behavior is ongoing. In some such embodiments, theconsumer analytics system may determine characteristics of consumers andtake action contemporaneously with a consumer's behavior by determiningthe characteristics and taking action when the consumer is predicted tobe about to engage in a behavior, when the consumer is determined to beengaging in the behavior, when the consumer is determined to haverecently ended a behavior, and/or when the consumer is detected to beabout to end a behavior. A consumer analytics system may take an actioncontemporaneously with a consumer's behavior when the consumer has notyet engaged in another behavior or moved in a manner from which theconsumer analytics system has determined another behavior of theconsumer.

As discussed above in connection with FIG. 2, a consumer analyticssystem may receive input defining any suitable action to be taken inresponse to any suitable condition(s). The condition(s) may relate toany suitable one or more characteristics of one or more consumersdetermined by a consumer analytics system from location data for one ormore consumers. The characteristic(s) that may be determined by theconsumer analytics system and that may satisfy conditions for an actionmay include one or more characteristics of a single consumer inferred orpredicted by the consumer analytics system. Additionally oralternatively, the characteristics may include one or morecharacteristics that are shared by consumers of a group of consumers andthat are inferred or predicted by the consumer analytics system, or oneor more characteristics of a group that are not associated with anyparticular consumer (e.g., an average characteristic for a group). Thecharacteristics that may be determined for one or more consumers may becharacteristics that relate to commercial activity of one or moreconsumers.

As mentioned above and as described in detail in the '280 applicationincorporated herein by reference, characteristics for one or moreconsumers that may be inferred or predicted by a consumer analyticssystem may include behavior characteristics, identity characteristics,or preference characteristics.

Behavior characteristics may include any suitable information regardingbehaviors of a consumer. Characteristics of behaviors may includeinformation about activities in which a consumer does or does notparticipate or a manner in which the consumer participates in anactivity. Information on a manner in which the consumer participates inan activity may include information on a frequency or periodicity of theconsumer's participation in the activity. Additionally, predictions ofwhether a consumer is likely to participate in an activity may beinferred or predicted as behavior characteristics. Behaviors of aconsumer may include retail-relevant behaviors and lifestyle-relevantbehaviors. Retail-relevant behaviors may include behaviors relating tocommercial activities engaged in by a consumer. Commercial activitiesmay include activities in which a monetary transaction takes place orcould take place, including visits to any location at which consumerscould purchase products or services. Lifestyle-relevant behaviors mayinclude information about consumers' work life, home life, and regularroutine, including their recreational behaviors. Lifestyle activitiesinclude visits to and time spent at a consumer's residence and place ofemployment; travel patterns and habits, including commuting patterns andair travel; and visits to outdoor recreation destinations, nightlifelocations, sports and entertainment venues, museums, amusement parks,tourist destinations, or other recreational destinations.

Identity characteristics may include demographic and socioeconomicattributes of a consumer. Demographic and socioeconomic attributes of aconsumer may include where a consumer lives, information about aconsumer's family, where a consumer works, and what a consumer does forwork.

Preference characteristics may include information on preferences of aconsumer regarding commercial activities and/or lifestyle-relevantactivities in which the consumer engages or desires to engage.Preference characteristics regarding commercial activities of a consumermay include preferences of the consumer for particular types of productsor services or particular products or services. Brand loyalties of aconsumer may be included in preference characteristics for the consumer.

For characteristics that a consumer analytics system is configured toinfer or predict based on location data, the consumer analytics systemmay also infer or predict a strength of the characteristic or alikelihood that the characteristic has been correctlyinferred/predicted.

Any characteristic of a consumer or group of consumers that isinferred/predicted by the consumer analytics system for the individualconsumer or for a group of consumers in which the consumer is includedmay be a condition of an action or may be evaluated to determine whetherone or more conditions have been satisfied. In examples described below,characteristics of a consumer that may trigger a consumer analyticssystem to take an action include behavior characteristics that relate tocommercial activity, including that relate to a commercial activity inwhich the consumer is engaging at the time the behavior characteristicsare identified. In some embodiments, identity and/or preferencecharacteristics may additionally or alternatively satisfy conditionsthat, when met, trigger the system to take an action. Further, whileexamples of behavior characteristics that may trigger an action aredescribed herein, it should be appreciated that characteristics of aconsumer related to any suitable behaviors may be used as conditions ofan action or evaluated to determine whether one or more conditions havebeen met. Examples of behaviors that, in embodiments, could trigger aconsumer analytics system to take actions when the systeminfers/predicts characteristics of a consumer related to the behaviorinclude (but are not limited to):

-   -   Outdoor recreational (hiking, biking, swimming, sailing, beach,        etc.);    -   Viewing or playing sports (baseball, football, golf . . . );    -   Watching a movie in a movie theatre;    -   Visiting a known location (like one's place-of-work or home);    -   Going inside a retail store, restaurant, convention center, or        other point of interest;    -   Driving past a retail, store, restaurant, convention center, or        other point of interest;    -   Traveling on a path that includes visits to particular stores,        such as a first store or store of a first type (e.g., a grocery        store) and a second store or store of a second type (e.g., a        department store that includes a grocery department);    -   Deviating from a behavioral pattern, such as by visiting a        setting or type of setting the consumer does not typically        visit;    -   Traveling toward a setting;    -   Making a purchase at a setting;    -   Moving in a trip that includes a visit to one setting or type of        setting and does not a visit to another setting or another type        of setting;    -   Driving past a billboard or other “Out of Home” (OOH)        advertisement;    -   Taking a trip by air, rail, car, bus, or boat; and    -   Any combination of the foregoing.        Examples of Types of Actions that May be Triggered

As consumer characteristics are predicted and/or inferred by a consumeranalytics system of a consumer analytics system, the consumer analyticssystem may take one or more actions when conditions for taking theactions are satisfied by the characteristics. Any suitable action may betaken. In some embodiments, information collection actions may betriggered by consumer characteristics meeting conditions for theactions. In some embodiments, information storage actions may betriggered by consumer characteristics meeting conditions for theactions.

An information collection action that may be taken by a consumeranalytics system may include collecting any suitable information fromany suitable source. In some cases, a consumer analytics system maycollect information from a consumer by soliciting information from theconsumers. Information may be solicited in any way, including by sendingmessages to a consumer requesting that the consumer perform a task. Inother cases, a consumer analytics system may collect information from adata source external to the consumer analytics system. information thatmay be collected by a consumer analytics system may include any suitableinformation, including information related to one or more commercialentities, products, and/or services. In some embodiments, a consumeranalytics system may collect information relating to commercialactivity. Information regarding commercial activity may relate tocommercial activity of a consumer and/or of a commercial entity.Information regarding a commercial activity may relate to a consumer, acommercial entity, and/or interactions between a consumer and acommercial entity. The information that is collected may be informationthat the consumer analytics system may evaluate to determinecharacteristics of a consumer and/or characteristics of a group ofconsumers related to commercial activity, such as behavior, identity, orpreference characteristics of a consumer or behavior, identity, orpreference characteristics shared by consumers of a group of consumers.

Examples of actions are described below, including examples of tasksthat a system may request a consumer perform and examples of informationthat may be retrieved by a system from an external data source ortransmitted to an external data store for storage. It should beappreciated, however, that embodiments are not limited to operatingaccording to these examples, as embodiments are not limited to takingany particular action or type of action in response to satisfaction ofone or more conditions.

FIG. 5 illustrates an exemplary technique that may be performed by aconsumer analytics system to solicit information from a consumer inresponse to conditions for the solicitation being met. Prior to thestart of the process 500 of FIG. 5, one or more actions and one or moreconditions for triggering the actions are specified to the consumeranalytics system, such as using techniques described above in connectionwith FIG. 2. The consumer analytics system may also register multipleconsumers with the system, which may include storing informationidentifying consumers and devices associated with consumers. In someembodiments, the information identifying consumers and devices may notpersonally identify consumers, through the information may uniquelyidentify consumers in the consumer analytics system. Once consumers areregistered with the system, location data for the consumers is receivedby the system and processed by a consumer analytics system of theconsumer analytics system.

The process 500 begins in block 502, in which the consumer analyticssystem of the consumer analytics system generates predictions and/orinferences of one or more characteristics of one or more consumers orgroups of consumers based at least in part on the location data for theconsumers. The consumers for which the characteristics are determined inblock 502 may be any suitable consumers. In some embodiments, theconsumers may be any consumers that are registered with the system andfor which the system receives location data. In other embodiments, theconsumers for which characteristics are determined in block 502 includeconsumers that are members of a pool of consumers that are subjects of amarket research study. The pool of consumers may be selected manuallyand/or automatically to have an assortment of characteristics, such asan assortment of identity characteristics. In some embodiments, as partof defining a market research study to be conducted, desiredcharacteristics of consumers to be included in a pool of market researchsubjects are identified.

The location data that is processed by the consumer analytics system inblock 502 may include any suitable location data. The location data mayinclude location data identifying measured physical locations ofconsumers and/or location data identifying settings visited by consumersand/or paths traveled by consumers. In some embodiments, the consumeranalytics system may analyze location data together with any othersuitable data, such as profile data for consumers identifyingcharacteristics previously defined for consumers.

The consumer analytics system may determine any suitable characteristicsas inferences and/or predictions in block 502, as discussed above. Thecharacteristics that may be inferred and/or predicted may includebehavior characteristics related to behaviors of consumers, includingbehaviors in which the consumers were engaging at the time the locationdata was generated. In addition, the consumer analytics system maydetermine the predictions and/or inferences at any time relative to atime at which the location data is received by the system. In someembodiments, location data may be received by the consumer analyticssystem as a stream of measurements of physical location that istransmitted to the consumer analytics system as the locations arevisited by the consumer, such that the consumer analytics systemreceives data indicating locations of consumers contemporaneously withthe consumers being present at the locations, in substantially realtime. In some such embodiments, the consumer analytics system may alsoanalyze the location data as the location data is received and generatepredictions and/or inferences in substantially real time. In embodimentsin which the characteristics are determined in substantially real time,the characteristics may be determined while the consumer is stillpresent at a location from which the characteristics were inferredand/or predicted, or while the consumer is traveling to the location ortraveling away from the setting. Though, in other embodiments, theconsumer analytics system may receive location data at any time and theconsumer analytics system may analyze location data at any time, asembodiments are not limited in this respect.

In block 504, the consumer analytics system compares the one or morecharacteristics for one or more consumers or groups determined by theconsumer analytics system to one or more conditions of one or moreactions that may be taken by the consumer analytics system. Thecharacteristics of consumers that are compared in block 504 may includecharacteristics inferred and/or predicted at any time by the consumeranalytics system, including characteristics inferred and/or predictedbased on different location data obtained at different times. Thecomparison may be carried out in any suitable manner. For example, insome embodiments a characteristic determined by the consumer analyticssystem may be compared to a condition to determine whether thedetermined characteristic matches the characteristic defined in thecondition. Such a matching may be carried out in cases where thecondition is that a type of characteristic be determined, such as abehavior characteristic indicating that a consumer visited a particularstore or visited a particular store on the way to work. As anotherexample of a comparison, in some embodiments a value of a characteristicmay be evaluated with respect to a value indicated by the condition.Such an evaluation may be carried out in cases where the condition isthat a quantitative or qualitative attribute of a characteristic have orexceed a particular value, such as a condition that a behaviorcharacteristic of a consumer indicate that the consumer spent more thantwo hours at a particular retail business. Any suitable comparison maybe carried out in block 504, as embodiments are not limited in thisrespect.

In block 506, the consumer analytics system determines whethercharacteristic(s) for a consumer or group of consumers satisfy thecondition(s) for an action to be taken. If not, the process 500 returnsto block 502, in which the consumer analytics system analyzes locationdata (which may include newly-received location data) to determinecharacteristics for consumers. If, however, the condition(s) for anaction are met, the consumer analytics system takes the action. In theexample of FIG. 5, the action taken by the consumer analytics system issoliciting information from a consumer. The characteristics of theconsumers determined in block 502 may have been determined for theconsumer that is to be solicited or may have been determined for otherconsumers or a group of consumers, including a group of which theconsumer to be solicited is a member or a group of which the consumer tobe solicited is not a member. Accordingly, in block 508, the consumeranalytics system compiles one or more messages to be transmitted to theconsumer soliciting the information. The message(s) may describe theinformation desired to be collected from the consumer. The system maythen transmit the message(s) to the consumer. The message(s) may be inany suitable format and include a request for any suitable information,including information relating to commercial activity. For example, therequest for information may be a request for the consumer to provideinformation on opinions or observations of the consumer, including byanswering survey questions. As another example, the request forinformation may be a request that the consumer interact with acommercial entity, such as by visiting the entity, moving around asetting of the commercial entity, and speaking with staff for acommercial entity. Such a request may include a request to provide mediaof a subject relating to a commercial entity to the consumer analyticssystem, such as an image, video, and/or audio of the subject. As anotherexample, the request for information may be a request for informationregarding purchasing behavior of the consumer, such as with respect to acommercial entity. The request may be a request that the consumer scanbarcodes of items purchased by the consumer and provide informationresulting from the scans to the system.

The message may be transmitted to the consumer by the consumer analyticssystem in any suitable manner. In some embodiments, the message may betransmitted to a consumer's smartphone, including to an applicationexecuting on the consumer's smartphone. In some embodiments in which themessage is transmitted to the consumer's smartphone, information about aconsumer stored by the consumer analytics system may not personallyidentify the consumer. In such a case, the consumer analytics system maynot have a phone number or other identifier for a consumer's phone thatmay be used by the system to transfer the message directly to theconsumer's phone. The consumer analytics system may thereforecommunicate to a cellular network or other service provider to requestthat the message be transmitted to the consumer's phone, or maycommunicate to any other suitable intermediary requesting that themessage be made available to the consumer's phone. In other embodimentsin which the message is transmitted to the consumer's phone, however,the consumer analytics system may be able to communicate directly to theconsumer's phone and transmit the data to the consumer's phone. In someembodiments, the message may not be transmitted to a consumer's phone,but may be transmitted to a consumer via electronic mail, instantmessage, or in any other suitable manner. Further, embodiments are notlimited to transmitting the message to the consumer at any particulartime. In some embodiments in which location data is received andanalyzed in substantially real time, the consumer analytics system maytransmit the message to the consumer in substantially real time. Bytransmitting the message to the consumer in substantially real time, theconsumer may receive the message while the consumer is still at alocation from which the consumer analytics system determinedcharacteristics that satisfied the conditions. In other embodiments,however, the message may be transmitted to the consumer at a later time.For example, in some embodiments, the consumer analytics system maygenerate messages and hold the messages until the consumer, anapplication executing on the consumer's phone, or another entityrequests that the messages be transmitted to the consumer.

In block 510, once the message(s) has been transmitted to the consumersoliciting information from the consumer, the consumer analytics systemmay wait for the consumer to provide the requested information. Uponreceiving the information from the consumer (which may include responsesto survey questions, media (e.g., an image, video, and/or audio), and/orany other requested information), in block 512 the consumer analyticssystem stores the received information. The information may be stored inconnection with a profile for the consumer or may be stored in any othersuitable manner. Once the information is stored, the process 500continues to block 502, in which the consumer analytics system processeslocation data (which may include newly-received location data) todetermine characteristics for consumers.

As a result of the process 500, the consumer analytics system storesinformation received from consumers. The information received fromconsumers may be retrieved and provided to any suitable entity,including market researchers, along with any other results from a marketresearch study. Other results may include one or more consumercharacteristics determined from location data. In some embodiments, datareceived from a consumer in response to a request for data may beanalyzed by the consumer analytics system to determine characteristicsfor the consumer. For example, if the consumer was asked to providedemographic information and brand preference information in response toa survey, the consumer analytics system may determine identity andpreference characteristics for the consumer by analyzing the datareceived from the consumer. As another example, if the consumer wasasked to provide the name of a commercial entity the consumer shops ator was asked to scan the barcode of a product purchased by the consumer,the consumer analytics system may determine behavior characteristics ofthe consumer from the name of the commercial entity or the product ownedby the consumer. In addition, information collected from multipleconsumers may be analyzed to determine trends or patterns in theinformation from the consumers. For example, data may be analyzed todetermine patterns with respect to characteristics of consumers whoprovided the information. The characteristics of the consumers may havebeen determined from location data for the consumers. Thus, the systemmay identify consumers who provided the same answer to a survey questionand who share one or more identity, behavior, and/or preferencecharacteristic.

As another result of the process of FIG. 5, the consumer analyticssystem may determine an incentive to provide to the consumer and providethat incentive to the consumer. The incentive may be a reward for theconsumer providing the solicited information to the consumer analyticssystem and/or an inducement for the consumer to provide more informationin the future, in response to future messages from the consumeranalytics system. The consumer analytics system may provide any suitableincentive, as embodiments are not limited in this respect. As oneexample of an inducement, the consumer analytics system may provide amonetary incentive to the consumer, such as by transmitting money to theconsumer in any suitable manner. For example, a payment may be made toan account for the consumer (e.g., a credit card account or bankaccount) or a check may be provided to the consumer. Monetaryincentives, when provided, may be provided to the consumer at any time,such as close in time to the consumer providing the information to theconsumer analytics system or at a later time, such as at a set interval,after the consumer has provided information a number of times, once theaggregate payment amount has reached a threshold, or upon request of theconsumer. As another example of an incentive, any suitable item otherthan money may be provided to the consumer, such as a coupon that isredeemable with a commercial entity. The consumer analytics system mayprovide any suitable coupon to the consumer in response to theconsumer's provision of information to the system. In some embodiments,the consumer analytics system may determine a commercial entity forwhich to offer a coupon from the characteristics determined for aconsumer based on location data for the consumer. For example, if theconsumer analytics system infers from location data that the consumer isa customer of a particular retail store, or predicts that the consumermay be interested in a particular retail store, a coupon for that retailstore may be provided. As another example, if the consumer analyticssystem determines that the consumer is at a setting corresponding to aparticular commercial entity, a coupon related to that commercial entitymay be provided to the consumer.

In the example of FIG. 5, the solicitation of information in response todetermining characteristics of a consumer was described generally. Twoexamples of tasks that a consumer analytics system may ask a consumer tocarry out when conditions are satisfied are described below inconnection with FIGS. 6-8. It should be appreciated, however, thatembodiments are not limited to operating in accordance with theseexamples.

FIG. 6 illustrates an example of a process that may be followed by aconsumer analytics system for soliciting information from a consumer. Aswith the process 500 of FIG. 5, prior to the start of the process 600 ofFIG. 6, the consumer analytics system may receive registration formultiple consumers, which may include storing information identifyingconsumers and devices associated with consumers. Once consumers areregistered with the system, location data for the consumers is receivedby the system and may be processed by the consumer analytics system.

The process 600 begins in block 602, in which the consumer analyticssystem is configured with an action and conditions for the action to betaken by the consumer analytics system. The consumer analytics systemmay be configured by an administrator. The system may be configured inresponse to a request by a market researcher for data to be collectedfrom one or more consumers. In the example of FIG. 6, the configurationof block 602 may include configuring the consumer analytics system totake an action that includes prompting a consumer to answer surveyquestions and provide a photograph to the system. The survey questionsmay relate to a consumer's opinions with respect to a retail business,reasons for shopping at the retail business, and opinions with respectto a particular product carried by the retail business. The picture maybe a photograph of shelves of the retailer holding the particularproduct. The market researcher may request the picture to determinewhether the consumer is able to quickly or conveniently locate the shelfcarrying the particular product. Survey questions may include a questionrelating to whether the consumer was able to locate the shelves for theproduct, and the picture may also be used by a market researcher toconfirm that the consumer actually located the product on the shelves.The configuration of block 602 may also include specifying conditionsthat, when met, trigger the consumer analytics system to transmit amessage to the consumer describing the survey and the desired picture.In the example of FIG. 6, the consumer analytics system may beconfigured to transmit the message to a consumer when the consumeranalytics system determines a behavior characteristic for a consumerindicating that the consumer visited the retail business.

In block 604, location data for a consumer is received by the consumeranalytics system and analyzed to generate inferences and/or predictionsregarding characteristics of consumers. As part of the analysis of block604, the consumer analytics system infers one or more behaviors of aconsumer, including that the consumer visited the retail business. Inaccordance with the configuration of block 602, when the consumeranalytics system determines the behavior characteristic indicating thatthe consumer visited the retail business, and determines in block 606that the behavior characteristic satisfies the condition, the consumeranalytics system in block 608 takes the action with which the system isconfigured. The consumer analytics system, in block 608, transmits amessage to a consumer's phone. The message may include any suitablecontent to solicit information from the consumer. The message mayinclude content regarding the task the consumer is being requested toperform, including a description of the task or an identifier for alocation (e.g., a URL for a web server) from which the description canbe obtained. The description of the task may include survey questionsthe consumer is requested to answer and a description of the photographthe consumer is requested to take and provide to the system.

Once the message is transmitted to the consumer, the consumer may begincompleting the task. The consumer may be presented with any suitableinterface for taking a survey as part of completing the task, an exampleof which is illustrated in FIG. 7. In the example of FIG. 7, a mobiledevice 701 used by the consumer displays the questions and possibleanswers of a survey 703 on the device's display screen 702. The mobiledevice 701 could be a mobile phone, tablet, or other device used by theconsumer. The consumer can then answer each question using the deviceand the responses are sent to the server. The questions and possibleanswers may be displayed on the device's screen by any suitable softwareexecuting on the device. For example, in some embodiments, anapplication dedicated to collecting location data and interacting withconsumer's to collect data from consumers for the consumer analyticssystem may be executing on the device, and the consumer may interactwith that application to complete the survey. It should be appreciatedthat, while multiple-choice questions are included in the example ofFIG. 7, surveys are not limited to including multiple-choice questions.Surveys may additionally or alternatively include open-ended questionsthat may be completed by a consumer. Additionally, as part of completingthe task, the consumer may obtain the photograph requested in the task,which may be done using a camera installed in the device used by theconsumer to complete the survey.

In block 610, the responses to the survey questions and the photographare received by the consumer analytics system from the consumer. Theconsumer analytics system may receive the responses and photographs inany suitable way, including by receiving them in one or morecommunications, via one or more communication networks, from the deviceon which the consumer completed the survey and obtained the photograph.When the data is received, the consumer analytics system may store theinformation in block 612. The data may be stored in any suitable manner,as embodiments are not limited in this respect. In the example of FIG.6, the data may be stored in association with a profile for theconsumer, and may be associated with information regarding theconsumer's interactions with the retail business. For example, the dataincluding the survey responses and the photograph may be stored inassociation with information identifying the time of day, week, or yearthe consumer visited the retail business, or an amount of time theconsumer spent at the retail business, which may be determined from thelocation data. As another example, the data may be stored in associationwith characteristics determined from the consumer's visit and pastvisits to the retail business, including a frequency of the consumer'svisits to the retail business, where the consumer was traveling to whenthe consumer stopped at the retail business, a purpose of the consumer'strip when the consumer visited the retail business (e.g., shopping for aparticular product, or a general shopping trip, or another purpose),other stores visited by the consumer during the same trip that theconsumer visited the retail business, or any other information that maybe determined by the consumer analytics system from the location datafor the consumer and/or from the data provided by the consumer.

Once the data is stored by the consumer analytics system, the process600 returns to block 604, in which the consumer analytics systemprocesses location data (which may include newly-received location data)to determine characteristics for consumers.

FIG. 8 illustrates an example of another process that may be followed bya consumer analytics system for collecting data from a consumer. As withthe process 600 of FIG. 6, prior to the start of the process 800 of FIG.8, the consumer analytics system may receive registration for multipleconsumers, which may include storing information identifying consumersand devices associated with consumers. Once consumers are registeredwith the system, location data for the consumers is received by thesystem and may be processed by a consumer analytics system of theconsumer analytics system.

The process 800 begins in block 802, in which the consumer analyticssystem is configured with an action and conditions for the action to betaken by the consumer analytics system. The consumer analytics systemmay be configured by an administrator. The system may be configured inresponse to a request by a market researcher for data to be collectedfrom one or more consumers. In the example of FIG. 8, the configurationof block 802 may include configuring the consumer analytics system toprompt a consumer to scan products purchased by a consumer during ashopping trip in which the consumer visited to one retail business, butdrove past another retail business. A market researcher working onbehalf of the retail business not visited by the consumer may desirethis information, such as in the case where the business visited by aconsumer is a competitor of the business not visited by the consumer.The retail business not visited by the consumer may desire to know whatproducts consumers buy at the competitor that the consumer visited. Theconfiguration of block 802 may also include specifying conditions that,when met, trigger the consumer analytics system to transmit a message tothe consumer requesting that the consumer scan the purchased products.In the example of FIG. 8, the consumer analytics system may beconfigured to transmit the message to a consumer when the consumeranalytics system determines behavior characteristics for a consumerindicating that the consumer visited the one retail business, drove pastthe other retail business, and returned home.

In block 804, location data for a consumer is received by the consumeranalytics system and analyzed to generate inferences and/or predictionsregarding characteristics of consumers. As part of the analysis of block804, the consumer analytics system infers one or more behaviors of aconsumer, including that the consumer visited the one retail business,drove past the other retail business, and returned home. Thesecharacteristics may be determined by the consumer analytics system overa period of time. For example, the consumer analytics system maydetermine substantially in real time, as the consumer moves, that theconsumer has visited one of the retail business and driven past theother retail businesses. At a later time, also substantially in realtime with the consumer's movements, the consumer analytics system maydetermine that the consumer has arrived at the consumer's home. Thus,the consumer analytics system may, in some embodiments, determine thedifferent characteristics that satisfy the conditions of an action atdifferent times. In other embodiments, however, the consumer analyticssystem may process a set of location data and determine thesecharacteristics at substantially the same time.

The determined characteristics of the consumer may then be compared toconditions of an action in block 806. If the determined characteristicsdo not satisfy the conditions, the process 800 returns to block 804.However, in accordance with the configuration of block 802, when it isdetermined in block 806 that the consumer analytics system determinedbehavior characteristics indicating that the consumer visited the oneretail business, drove past the other retail business, and arrived home,the consumer analytics system takes the action in block 808. Theconsumer analytics system, in block 808, transmits a message to aconsumer's phone. The message may include any suitable content regardingthe task the consumer is being requested to perform, including adescription of the task or an identifier for a location (e.g., a URL fora web server) from which the description can be obtained. In the exampleof FIG. 8, the description of the task may include a request that theconsumer scan the items purchased by the consumer at the retail businessvisited by the consumer.

The scan requested of the consumer of the products may be any suitablescan. In some cases, the consumer may be requested to scan a bar code(which may be a Universal Product Code (UPC) or Quick Response (QR)Code) or a Near Field Communications (NFC) tag (which may be a RadioFrequency Identification (RFID) tag) for a product and provide to theconsumer analytics system the information obtained through the scanning.The information obtained through the scanning may include informationidentifying the product with which the UPC, QR code, or NFC tag isassociated. Thus, by scanning the items, the consumer may obtain anelectronically-stored list of items purchased by the consumer. If theconsumer's phone includes a bar code or NFC scanner, the consumer mayuse the phone to scan and provide the information to the consumeranalytics system.

In block 810, the data obtained by the consumer (e.g., the list ofpurchased items) through the scanning is received by the consumeranalytics system from the consumer. The consumer analytics system mayreceive the data in any suitable way, including by receiving the data inone or more communications from the consumer's phone. When the data isreceived, the consumer analytics system may store the information inblock 812. The data may be stored in any suitable manner, as embodimentsare not limited in this respect. In the example of FIG. 8, the data maybe stored in association with a profile for the consumer, and may beassociated with information regarding the consumer's interactions withthe retail business that the consumer visited. For example, the dataincluding the list of products may be stored in association withinformation identifying the time of day, week, or year the consumervisited the retail business, or an amount of time the consumer spent atthe retail business, which may be determined from the location data. Asanother example, the data may be stored in association withcharacteristics determined from the consumer's visit and past visits tothe retail business visited by the consumer, including a frequency ofthe consumer's visits to the retail business, where the consumer wastraveling to when the consumer stopped at the retail business, a purposeof the consumer's trip when the consumer visited the retail business(e.g., shopping for a particular product, or a general shopping trip, oranother purpose), other stores visited by the consumer during the sametrip that the consumer visited the retail business, or any otherinformation that may be determined by the consumer analytics system fromthe location data for the consumer and/or from the data provided by theconsumer.

Once the data is stored by the consumer analytics system, the process800 returns to block 804, in which the consumer analytics systemprocesses location data (which may include newly-received location data)to determine characteristics for consumers.

Examples of actions that may be taken by a consumer analytics system tocollect data from a consumer have been described above. It should beappreciated, however, that embodiments in which a consumer analyticssystem may collect data from a consumer are not limited to requestingthat consumers carry out any particular task to provide any particulardata or type of data. Rather, a consumer analytics system may requestthat a consumer provide any suitable data to the consumer analyticssystem. Thus, examples of tasks that a consumer may be asked to completeinclude:

-   -   Answering questions—message(s) may be sent to one or more        consumers requesting that they answer survey questions. The        surveys could be delivered via an application running on a        mobile device, an application on a desktop or laptop computer,        or any other common way of sending a survey. In some        implementations, the messages sent to consumers could contain        the survey questions themselves.    -   Performing a physical action—message(s) may be sent to one or        more consumers requesting that they perform some physical action        either within the POI or at a separate location. In some        implementations this would involve interacting with a POI (e.g.        take a coupon from a display within a store and mail it to a        specific address) or other people within a POI (e.g. ask a        service desk attendant for help, ask another consumer her        opinion, etc.).    -   Capturing media—actions may include capturing and storing        various kinds of electronic media (e.g. photos, video, audio,        etc.). In some implementations, an application on the mobile        device 201 could automatically capture the media using sensors        on the device (e.g. a camera, microphone, etc.). In other        implementations, the consumer could be provided with a message        requesting her to capture the media.

As mentioned above, it should also be appreciated that embodiments arenot limited to soliciting information from consumers. In someembodiments, a consumer analytics system may additionally oralternatively acquire data from one or more data sources external to theconsumer analytics system. The data sources external to the consumeranalytics system may be sources of electronically-stored data. Theconsumer analytics system may be able to acquire the data bycommunicating with the data sources via one or more communicationnetworks, including one or more wide-area networks, such as theInternet.

FIG. 9 illustrates an exemplary technique that may be performed by aconsumer analytics system to collect data from one or more external datasources in response to conditions for the collection being met. Prior tothe start of the process 900 of FIG. 9, one or more actions and one ormore conditions for triggering the actions are specified to the consumeranalytics system, such as using techniques described above in connectionwith FIG. 2. The consumer analytics system may also receive registrationfor multiple consumers, which may include storing informationidentifying consumers and devices associated with consumers. Onceconsumers are registered with the system, location data for theconsumers is received by the system and processed by the consumeranalytics system.

The process 900 begins in block 902, in which the consumer analyticssystem generates predictions and/or inferences of one or morecharacteristics of one or more consumers based at least in part on thelocation data for the consumers. The predictions and/or inferences ofblock 902 may be generated in any suitable manner, including accordingto techniques discussed above in connection with block 502 of FIG. 5.

In block 904, the consumer analytics system compares one or morecharacteristics for one or more consumers determined by the consumeranalytics system to one or more conditions of one or more actions thatmay be taken by the consumer analytics system. The characteristics ofconsumers that are compared in block 904 may include characteristicsinferred and/or predicted at any time by the consumer analytics system,including characteristics inferred and/or predicted based on differentlocation data obtained at different times. The comparison of block 904may be carried out in any suitable manner, including according totechniques discussed above in connection with block 504 of FIG. 5.

In block 906, the consumer analytics system determines whether thecharacteristic(s) for a consumer satisfy the condition(s) for the systemto take an action with which the system is configured. If not, theprocess 900 returns to block 902, in which the consumer analytics systemanalyzes location data (which may include newly-received location data)to determine characteristics for consumers. If, however, thecondition(s) for an action are met, the consumer analytics system takesthe action with which the system was configured. In the example of FIG.9, the action taken by the consumer analytics system includes collectingdata from one or more external data sources. Accordingly, in block 908,the consumer analytics system communicates with the one or more externaldata sources via one or more communication networks, which may includethe Internet, and requests that data stored by the external datasource(s) be provided to the consumer analytics system.

Any suitable data may be stored by the external data source(s) and,thus, any suitable data may be requested by the consumer analyticssystem. Examples of data include data relating to a consumer, a setting,or an environment of a consumer's interactions with a setting.

As an example of data regarding a consumer that may be collected, insome embodiments, the external data sources may include a socialnetworking service storing social networking data, including socialnetworking data provided by the consumer to the social networkingservice. Embodiments may interact with any suitable social networkingservice of any type to acquire data from the social networking service.In some implementations, the social networking service may be a text andmedia social sharing service such as FACEBOOK®, a social locationsharing service such as FOURSQUARE®, a social task-assignment servicesuch as SCVNGR®, a gaming service SHADOW CITIES™ by GREY AREA™, a shortmessage distribution service such as TWITTER®, or any other suitablesocial network. In cases in which data is acquired from a socialnetworking service, the consumer analytics system may, in block 908,retrieve social networking data provided by the consumer to the socialnetworking service. In embodiments that collect social networking data,information regarding a social networking service used by a consumer maybe collected as part of registering a consumer with the consumeranalytics system. Information about the social networking service mayinclude an identification of the service, a consumer's username or otheridentifier for the service, and any other suitable information. Socialnetworking data may be relevant to a market research study because theinformation may identify behaviors and/or preferences of a consumer. Forexample, if a consumer mentions a commercial entity, product, or servicein social networking data provided by the consumer, the consumeranalytics system may be able to determine behavior and/or preferencecharacteristics for the consumer. Social networking data provided by theconsumer to the social networking service during a time period thatcorresponds to a time period for which the consumer was at a setting maybe of interest to market researchers, as the social networking data mayrelate to the consumer's experiences at the setting. As another exampleof social networking data, if a consumer who was detected to havevisited a setting posts information to a social networking serviceidentifying places visited by the consumer, the consumer analyticssystem may be able to check the setting identified by the consumeranalytics system based on the location data against the places listed bythe consumer to confirm that the consumer visited the setting identifiedby the consumer analytics system. In embodiments in which a consumeranalytics system collects social networking data, any suitable socialnetworking data, including textual information and/or media information(e.g., images) may be collected by the system.

As another example of data relating to a consumer that may be obtainedfrom external data sources, information identifying a manner in which aconsumer uses a mobile phone or other device may be collected. Suchinformation may be collected from an application executing on a devicethat performs “on-device metering” (ODM), may be collected from anoperator of a cellular network by which the device communicates, or fromany other suitable entity. The information regarding the consumer'sdevice use may include information on which applications are opened andat what time; call histories; logs of text messaging and/or multimediamessaging; website/email activity; and any other information aboutactivities of the consumer regarding the device. Applications that areexecuted on a device operated by a consumer may include applicationsrelated to social networking services.

As another example of data relating to a consumer that may be obtainedfrom external data sources, purchasing data may be obtained by a system.In some embodiments a consumer analytics system may be configured tocombine location data for a consumer with the consumer's purchasingactivity. Combining location data for a consumer with the consumer'spurchasing activity may enable analysis of characteristics of consumersat a finer grain. For example, a brand manager may be interested in datacollected from consumers who visited a retail business, but may be moreinterested in data collected from consumers who visited the retailbusiness and actually purchased a product of the brand of interest.Alternatively, a brand manager may be interested in comparing datacollected from consumers who did not purchase a product of the brand toconsumers who did purchase a product of the brand. Accordingly, in someembodiments the consumer analytics system may be configured tocommunicate with one or more external data sources to determineinformation about a consumer's purchasing activity. For example, theconsumer analytics system may communicate with a data store ofinformation about a consumer's purchases that includes data on whichproducts the consumer purchased, how much each product cost, the time ofday for each purchase, whether or not the item was on sale, the barcodeor product photograph or any other unique product identifier, andinformation about how the product(s) were paid for. In some embodiments,a consumer analytics system may collect data about the consumer'spurchasing activity from a household consumer panel, such as theNielsen-IRI National Consumer Panel or the Kantar Retail WorldPanel.These panels invite consumers to scan the barcodes of every item theypurchase out of home and provide the foregoing list of information aboutthe purchase. As another example, information about a consumer'spurchasing activity may be collected by the consumer analytics systemfrom one or more data stores associated with the consumer's creditand/or debit card accounts.

Data that is collected from external data sources may additionally oralternatively include information regarding a setting, such asinformation regarding a commercial entity. For example, advertisements,sales or promotions, catalogs, circulars, or other information regardingmarketing efforts of a commercial entity may be collected. Informationregarding marketing efforts of a commercial entity may be relevant insome cases because the marketing information may provide context for aconsumer's visits to the commercial entity, such as when a particularproduct is on sale and a consumer indicates (such as in response to asurvey, as discussed above) that the consumer visited the commercialentity primarily to purchase the product. As another example ofinformation regarding a setting, data collected by loyalty program for acommercial entity may be collected by the consumer analytics system. Aloyalty program for a commercial entity may be a program established bya commercial entity for tracking and interacting with customers of thecommercial entity. One example of a loyalty program is afrequent-shopper program for a commercial entity. A data store for aloyalty program may store information identifying a manner in which aconsumer interacts with a commercial entity, including a log oftransactions between the consumer and the commercial entity that mayidentify products purchased and times at which the products werepurchased, and/or coupons or other discounts offered to the consumer aspart of the program.

Data that relates to both a consumer and a setting may also be collectedin some embodiments from an external data store. For example, dataindicating a movement and/or behavior of a consumer inside a setting maybe collected by the consumer analytics system from an external datasource. The consumer analytics system may be configured to combinelocation data that is sufficiently accurate to determine settingsvisited by a consumer (e.g., GPS data) with more precise location datathat identifies how the consumer moves around within a given setting,such as how the consumer moves around indoors at a setting. Indoorlocation data relating to a consumer's movements at a commercial entitymay be derived using technologies other than GPS, such as technologiesoffering indoor location accuracy above that available using GPS orother satellite-based system. Examples of location-determiningtechnologies that may be used include RFID, video, audio, and infraredtechnologies. When the consumer analytics system obtains from anexternal data source indoor location data for a consumer, the consumeranalytics system may process the data to identify a consumer's paththrough the setting, including dwell times in certain aisles of a storeor in front of certain product displays or signage of store. Indoorlocation data, when processed, may also indicate the amount of time aconsumer spent waiting at the checkout register of a store, and/orvarious metrics to indicate how busy a store is at a given time of day.

Indoor location data may be relevant to market researchers. As anexample, by combining location analytics with in-store locationbehaviors, it may be possible to segment consumers' out-of-storeactivity according to the specific categories of product that are ofinterest to the market researcher. It is also possible to segmentconsumers and data collected from consumers (e.g., responses to surveys)according to how consumers move around a store. For example, a retailermay be interested only in the customer satisfaction metrics forconsumers who are required to wait at the checkout register for morethan 10 minutes because the store is exceedingly busy. As a secondexample, a brand manager may be interested in the in-the-moment mobilesurvey responses only of consumers who exhibited dwell-time within theaisle of a store that showcases a given product category of interest.

Data that is collected from external data sources may additionally oralternatively include information regarding an environment forinteractions between a consumer and a setting. Environmental informationmay include any information regarding circumstances that may affectcommercial activity, such as information regarding circumstances thatmay affect a monetary transaction or a potential monetary transactionbetween a consumer and a commercial entity. The circumstances may nothave a direct relationship to the consumer or to the commercial entity.For example, weather data may be collected in some embodiments. Weatherinformation may affect transactions because some consumers may stay homeduring a rainstorm, heat wave, or other weather event, while someconsumers may spend more time in a shopping mall or movie theater duringsuch a weather event, or make emergency trips to stores in advance of aweather event. Gas prices are another example of environmentalinformation that may be collected in some embodiments. Gas prices mayaffect transactions because some consumers may stay closer to home ormake fewer trips when gas prices are high, and conversely may make moretrips or longer trips when gas prices are low.

Any one or more of these examples of types of data, or any othersuitable data, may be collected from external data sources by a consumeranalytics system in block 908. In response to a request for datatransmitted by the consumer analytics system in block 908, in block 910the consumer analytics system may receive the data as one or morecommunications received via a network, including a wide-area network,from the external data store. In block 912, the system stores the data.The data may be stored in connection with a profile for the consumer ormay be stored in any other suitable manner. Once the information isstored, the process 900 continues to block 902, in which the consumeranalytics system processes location data (which may includenewly-received location data) to determine characteristics forconsumers.

As a result of the process 900, the consumer analytics system storesdata received from external data sources. The data received from theexternal data sources may be retrieved and provided to any suitableentity, including market researchers, along with any other results froma market research study such as consumer characteristics. In someembodiments, data received from the external data sources may beanalyzed by the consumer analytics system to determine characteristicsfor the consumer. For example, if social networking data or text messagesent by a consumer includes a mention of a commercial entity or product,that data may be analyzed to generate predictions and/or inferences ofcharacteristics of the consumer with respect to the commercial entity orproduct.

Examples of data that may be collected by a consumer analytics systemfrom one or more external data sources have been described above. Itshould be appreciated, however, that embodiments in which a consumeranalytics system may collect data from external data sources are notlimited to collecting any particular data or type of data. Rather, aconsumer analytics system may collect any suitable data. Thus, examplesof data that a consumer analytics system may collect from one or moreexternal data sources include:

-   -   Data related to a consumer: As an example, when the system        discovers a consumer has gone hiking, information the consumer        had published on a social networking website (e.g. Facebook.com,        Friendster.com, hi5.com, Orkut.com, etc.) can be collected. As        this data collection event is triggered close to the time the        consumer performed the behavior of interest, the collected data        may be tied to the event.    -   Data related to a consumer behavior: As an example, if the        system discovers a consumer has been line dancing at a specific        venue in Austin, Tex., social media websites could be searched        for any text or media published by users near the consumer's        location and associated with line dancing. This method could be        used to calculate how common the given behavior is (e.g. how        many other people are line dancing in Austin), or to associate        this consumer with other consumers, places, or locations, or        other types of measurements.

As mentioned above, embodiments are not limited to taking datacollection actions when consumer characteristics meet conditions for theconsumer analytics system to take an action. In some embodiments, ratherthan an action being the collection of data when consumercharacteristics meeting conditions, a consumer analytics system may actto transmit data when consumer characteristics meet conditions for theaction to be taken.

FIG. 10 illustrates an exemplary technique that may be performed by aconsumer analytics system to collect data from a consumer in response toconditions for the collection being met. Prior to the start of theprocess 1000 of FIG. 10, one or more actions and one or more conditionsfor triggering the actions are specified to the consumer analyticssystem, such as using techniques described above in connection with FIG.2. The consumer analytics system may also receive registration formultiple consumers, which may include storing information identifyingconsumers and devices associated with consumers. In some embodiments,the information identifying consumers and devices may not personallyidentify consumers, but may uniquely identify consumers in the consumeranalytics system. Once consumers are registered with the system,location data for the consumers is received by the system and processedby the consumer analytics system.

The process 1000 begins in block 1002, in which the consumer analyticssystem generates predictions and/or inferences of one or morecharacteristics of one or more consumers based at least in part on thelocation data for the consumers. The predictions and/or inferences ofblock 1002 may be generated in any suitable manner, including accordingto techniques discussed above in accordance with block 502 of FIG. 5.

In block 1004, the consumer analytics system compares one or morecharacteristics for one or more consumers determined by the consumeranalytics system to one or more conditions of one or more actions thatmay be taken by the consumer analytics system. The characteristics ofconsumers that are compared in block 1004 may include characteristicsinferred and/or predicted at any time by the consumer analytics system,including characteristics inferred and/or predicted based on differentlocation data obtained at different times. The comparison of block 1002may be carried out in any suitable manner, including according totechniques discussed above in accordance with block 504 of FIG. 5.

In block 1006, the consumer analytics system determines whether thecharacteristic(s) for a consumer satisfy the condition(s) for an actionto be taken. If not, the process 1000 returns to block 1002, in whichthe consumer analytics system analyzes location data (which mayincluding newly-received location data) to determine characteristics forconsumers. If, however, the condition(s) for an action are met, theconsumer analytics system takes the action. In the example of FIG. 10,the action taken by the consumer analytics system is transmitting datato one or more external data stores. The external data stores may storedata electronically, and the consumer analytics system may be able totransmit data to the data stores electronically, via one or morecommunication networks, including a wide-area network such as theInternet. The external data stores may store any suitable information,including information regarding a consumer and/or information regardinga setting.

In some embodiments, a consumer analytics system may transmit data to adata store of information regarding a consumer that is a socialnetworking service storing social networking data, including socialnetworking data provided by the consumer to the social networkingservice. In such a case, the consumer analytics system may, in block1008, transmit social networking data to the social networking service.In embodiments that transmit social networking data, informationregarding a social networking service used by a consumer may becollected as part of registering a consumer with the consumer analyticssystem. Information about the social networking service may include anidentification of the service, a consumer's username or other identifierfor the service, and any other suitable information. Information that istransmitted to the social networking service may include any suitableinformation transmitted on behalf of any suitable party. In someembodiments, the consumer analytics system may transmit information tothe social networking service on behalf of the consumer. For example,when the consumer is detected to have visited a setting, the consumeranalytics system may transmit information to the social networkingservice, on behalf of the consumer, indicating that the consumer was atthe setting. When the information is received by the social networkingservice, a profile for the consumer may be updated to reflect that theconsumer was present at the setting. As another example of informationthat may be transmitted, the consumer analytics system may transmit datato a social networking service on behalf of a commercial entity. Theinformation transmitted on behalf of the commercial entity may include amessage directed to the consumer via the social networking service thatwould appear to come from the commercial entity, thanking the consumerfor visiting or providing any other suitable information. As anotherexample, the information transmitted on behalf of the commercial entitymay include a message that is not directed to any particular consumer,but is posted to the social networking service in response to consumercharacteristics indicative of consumer behavior. For example, an actionwith which the consumer analytics system may be configured may beposting a message regarding how busy a commercial entity is whenmultiple consumers are detected as visiting the commercial entity. Whenthe information is transmitted to the social networking service onbehalf of the commercial entity, a profile for the commercial entitymaintained by the social networking service may be updated to reflectthe transmitted information. As another example of information that maybe transmitted, information may be transmitted to the social networkingservice on behalf of the system and added by the social networkingservice to a profile maintained by the social networking service for thesystem.

As another example of information that may be transmitted to a datastore associated with a consumer, in some embodiments a consumeranalytics system may transmit coupons, descriptions of specials, orother promotional material to a consumer. The information may betransmitted by the consumer analytics system to a data store associatedwith the consumer, such as by transmitting the information to an e-mailaccount for the consumer or in the form of a message to be received by adevice operated by the consumer.

As another example of the type of data that may be transmitted, in someembodiments a consumer analytics system may transmit data to a datastore of information regarding a setting. A data store of informationregarding a setting may include information regarding a commercialentity, such as information regarding a loyalty program for thecommercial entity. A data store for a loyalty program may maintaininformation on coupons or discounts to offer to consumers, which may bedistributed to consumers in accordance with the loyalty program. In someembodiments, the consumer analytics system may, when characteristics ofa consumer satisfy conditions for an action with which the system isconfigured, transmit information to the data set for the loyalty programindicating that a consumer should be presented with a particular couponor discount.

Any one or more of these examples of types of data, or any othersuitable data, may be transmitted by the consumer analytics system inblock 1008. Once the data is transmitted by the system, the process 1000continues to block 1002, in which the consumer analytics systemprocesses location data (which may include newly-received location data)to determine characteristics for consumers.

Triggering Actions Based on Predicted Behaviors or Behaviors of Multipleor Other Consumers

In examples given above, a consumer analytics system is described astaking an action in response to inferring behavior characteristics ofone consumer relating to behaviors in which the consumer previouslyengaged or is engaging. It should be appreciated that embodiments arenot limited to taking action based on inferred characteristics relatingto current or past behaviors of a consumer. It should also beappreciated that embodiments are not limited to taking action based oncharacteristics of only a single consumer.

A consumer analytics system may determine characteristics of consumersby inferring the characteristics and/or by predicting characteristics. Aconsumer analytics system may take action based on either or both ofinferred characteristics and predicted characteristics of one or moreconsumers. Thus, as an alternative to or in addition to triggeringactions based on discovered behaviors of consumers, actions of aconsumer analytics system may also be triggered by behaviors in whichconsumers are predicted to engage based on historical data. This canallow the system to trigger actions before a certain behavior isexpected to take place.

For example, a researcher may be interested in better understanding howa consumer chooses which shampoo to buy. By processing a givenconsumer's location data while the consumer is moving, a probabilisticmodel can be created to (1) determine the stores the consumer shops atwhich carry the shampoo, and (2) given her most recent data, how likelyshe is to be on her way to a store that carries shampoo. When the modeldetects that there is a high probability that she is on her way to astore that carries shampoo, a “collect shampoo sentiment” action couldbe triggered. This could send the consumer a message (as an SMS, email,alert in an application, etc.) which could prompt her to answer surveyquestions to understand her mindset as she enters the store. Inaddition, other data sources, like transactions recorded for a loyaltyprogram, “Checkins” to social location-based applications, etc., couldbe polled to pull in more information.

As another example, when the consumer analytics system determines thatsome or many consumers included in a group of consumers are engaging ina behavior, the consumer analytics system may predict that otherconsumers included in the group may engage in that behavior soon or inthe future. A group of consumers may be defined in any suitable manner,including according to one or more characteristics that are shared byconsumers of the group. For example, a group of consumers may beconsumers who are customers of a particular store or who live in aparticular area. When the consumer analytics system detects fromanalyzing location data for some consumers included in a group thatthose consumers are engaged in a particular behavior, the consumeranalytics system may infer a behavior characteristic for those consumersand may predict the same or a similar behavior characteristic for otherconsumers of the group. Based on these predicted attributes for theconsumers, the consumer analytics system may take an action. Forexample, a group of consumers that share the behavior characteristic ofbeing customers of a retailer having locations across the United Statesmay be monitored by the consumer analytics system. The system maydetermine that many consumers of the group who also share the identitycharacteristic of living in the Eastern United States time zone arevisiting the retailer on a particular morning. The system may predict,based on this behavior of the Eastern consumers, that consumers of thegroup who share the identity characteristic of living in the Pacifictime zone will visit the retailer once it is morning in the Pacific timezone, and may take an action based on this prediction. For example, theconsumers may be provided with a survey based on the predicted behaviorcharacteristic.

In addition, conditions for an action to be taken by the consumeranalytics system may be based on characteristics for more than oneconsumer. For example, a condition for an action may specify that theaction is to be taken when a characteristic is inferred or predicted formultiple consumers, such as more than a threshold number of consumers.

Feedback Loop: Data from Actions Optimizing which Actions are Triggeredin the Future

In some embodiments, conditions associated with an action with which theconsumer analytics system is configured may be changed over time. Forexample, the system may optimize which actions should be triggered forwhich consumers based on prior actions. In addition, the system may havea feedback loop: as actions trigger collections more data (either fromthe consumer or other data sources), the resulting data may be fed backinto the system to adjust conditions that determine when a given actionis triggered.

For example, if a market researcher is interested in consumer sentimenton a given product, at a start of a market research study, a conditionfor a survey action may be defined such that every consumer who isdetermined by the consumer analytics system as having a high likelihoodof going to a store which carries the product may be surveyed. Thesurveys can be used to determine characteristics of consumers whoactually buy the product. Conditions for the survey action may then bechanged such that future surveys can be targeted at only consumers whohave characteristics matching those likely to buy the product. Forexample, demographic or preference characteristics may be determined forconsumers who buy the product may be determined. Conditions for theaction may then be set such that consumers who have the demographic orpreference characteristics and are determined to have the behaviorcharacteristic of being likely to visit a store that carries the productmay be surveyed.

Feedback Loop: Adjusting a Visit Detection Process Based on CollectedData

A visit detection process that may be used in some embodiments to matchconsumer location data to one or more settings, including one or morepoints of interest, is discussed above in connection with FIG. 4. Thevisit detection process is described above as identifying settings bycomparing a location indicated by location data to a definition ofsettings known to the visit detection process, such as definitions ofpoints of interest of a point of interest data set. As discussed above,the definition of a setting may include information identifying thesetting and a category of the setting, a location polygon identifyingboundaries of the setting, and information indicating operational hoursof the setting. In some embodiments, a consumer analytics system may beable to edit a definition of a setting based on information received inresponse to actions taken by the consumer analytics system. Editing thedefinition of a setting may form a part of adjusting a visit detectionprocess of the consumer analytics system based on the informationreceived in response to the actions taken by the consumer analyticssystem.

FIG. 11 illustrates an example of a process that may be carried out by aconsumer analytics system to adjust a visit detection process used bythe consumer analytics system. Prior to the start of the process 1100 ofFIG. 11, a consumer analytics system may be configured with a visitdetection process and point of interest data set. The process with whichthe system is configured may be the process described above inconnection with FIG. 4.

The process 1100 begins in block 1102, in which the consumer analyticssystem applies the visit detection process to location data obtained fora consumer. The visit detection process may be applied in block 1102 inany suitable manner, including as discussed above in connection withFIG. 4. In applying the visit detection process in block 1102, theconsumer analytics system attempts to identify a setting correspondingto a location indicated by the location data for the consumer. Theconsumer analytics system may, in block 1102, identify a setting orproduce information indicating that the setting could not be identified.

Based on the location data for the consumer, the consumer analyticssystem may take one or more actions, as discussed above. Actions takenby the consumer analytics system may include collecting data from aconsumer and/or from one or more external data sources. Data collectedfrom the consumer or from an external data source may includeinformation identifying a place visited by the consumer that correspondsto the location indicated by the location data evaluated in block 1102.For example, a survey sent to a consumer may request that the consumeridentify the place visited by the consumer, and the consumer mayidentify the place in response. As another example, a consumer mayprovide to a consumer analytics system, without prompt, an indicationthat the consumer is present at a location that the consumer analyticssystem should match to a setting, such as by providing the consumeranalytics system with an indication that the consumer is at a retailstore. In response, the consumer analytics system may obtain informationrelated to the setting corresponding to the consumer's location, such asby prompting the consumer to identify the setting. As another example ofa way in which the consumer analytics system may receive an indicationof a setting visited by a consumer, data collected by a consumeranalytics system from an external data source may include purchasingdata that may indicate a place at which a consumer made a purchase andmay include information about a product or service purchased by theconsumer. As another example, data collected by the consumer analyticssystem from an external data source may include social networking datathat may identify a place visited by the consumer. Accordingly, in block1104, the consumer analytics system receives from the consumer or theexternal data source a secondary indication of the setting visited bythe consumer.

In block 1106, the consumer analytics system compares the settingidentified through the visit detection process in block 1102 and thesetting identified in the secondary indication received in block 1104 todetermine whether the settings are the same or different. If theconsumer analytics system determines that the settings are the same,then the consumer analytics system may continue to block 1108. If,however, the consumer analytics system determines that the settings aredifferent, or in the case that the visit detection process was unable toidentify a setting in block 1102, the consumer analytics system maycontinue to block 1110.

In block 1108, in response to determining that the setting identified bythe visit detection process in block 1102 matches the setting identifiedin the second indication received in block 1104, the consumer analyticssystem may determine that the location visited by the consumer has beenconfirmed to be the setting. In response to determining that thelocation visited by the consumer has been confirmed to be the setting,the consumer analytics system may adjust a visit detection process. Theadjustment to the visit detection process may be done to increase alikelihood that the location data would be matched to the setting againin the future. The location data that is used to adjust the visitdetection process may be any suitable location data. In someembodiments, the location data may be one or more units of location dataon which an anchor, which was determined to correspond to the setting,was determined. In other embodiments, the location data may be locationdata for an anchor calculated from multiple units of location data, suchas a mean location determined from multiple units of location data thatmake up a cluster of location data for an anchor.

In some embodiments, adjusting the visit detection process may includeadjusting a definition of the setting. For example, a polygon definingthe boundaries of the setting may be adjusted based on the location datafrom which the consumer was determined to have visited the setting. Thepolygon may be adjusted in the case that the location data indicates alocation near the edges of or even outside of the polygon. In such acase, the polygon may be edited such that the location indicated by thelocation data falls within the polygon. As another example of anadjustment that may be made to the definition of a setting, if adefinition of a setting includes operating hours of the setting, thehours may be adjusted. In particular, if a time at which the locationdata was obtained falls outside of the operating hours of the settingindicated by the definition of the setting, the operating hours may beadjusted to include the time at which the location data was obtained. Asanother example of an adjustment that may be made to the definition of asetting, if a definition of a setting includes an indication of one ormore categories of the setting, the category(ies) may be adjusted. Acategory of a setting may be defined in any suitable manner and with anysuitable degree of specificity, as embodiments are not limited in thisrespect. In some embodiments, a category of a business that sells men'sclothing may be defined as “retail store,” while in other embodiments acategory may be defined as “clothing store” or “men's clothing store.”If data collected by the consumer analytics system indicates that abehavior of the consumer at the setting is inconsistent with thecategory indicated by the definition for the setting, the category maybe adjusted. For example, if purchasing data for a consumer indicatesthat the consumer purchased women's clothing at a setting for which thecategory is indicated to be “men's clothing store,” the category may beadjusted to additionally include (or alternatively include) “women'sclothing store.”

In addition to or as an alternative to adjusting a definition of thesetting, one or more thresholds of the visit detection process may beadjusted. As discussed above, as part of determining whether a consumervisited a setting, a consumer analytics system carrying out the visitdetection process may determine a likelihood that a cluster of locationdata corresponds to a setting. The calculated likelihood may then becompared to a threshold and, if the likelihood exceeds the threshold,the cluster of location data may be determined to correspond to thesetting. In some embodiments that adjust a visit detection process, thethreshold to which the likelihood is compared may be adjusted. Forexample, in block 1108, the threshold may be lowered in response todetermining that the location has been confirmed to match the setting.By lowering the threshold, the visit detection process may be morelikely to match the location data to the setting.

Once the visit detection process has been adjusted in block 1108, theprocess 1100 ends.

If, however, the consumer analytics system determines in block 1106 thatthe setting identified by the visit detection process does not match thesetting identified in block 1104, or if a setting was not identified bythe visit detection process, then the consumer analytics system mayadjust the visit detection process in block 1110. The adjustment ofblock 1110 may include adjustments similar to the adjustments of block1108. For example, when the visit detection process erroneously matcheslocation data to a setting, a threshold associated with the setting maybe raised such that the visit detection process is less likely to matchlocation data to the setting in the future. As another example,adjustments may be made to the polygon of a setting in block 1110, suchthat the polygon may include a location detected for the consumer whenthe consumer visited the place. However, in some embodiments, theconsumer analytics system may not adjust the polygon based on a singledata point in block 1110. The adjustment of block 1110 is made inresponse to detecting a mismatch between the setting identified in block1102 and the setting identified in block 1104. The basis of thediscrepancy may not be known, however. The location data and/or thesetting identified in block 1104 may be erroneous. It may be undesirableto adjust the visit detection process based on erroneous data. Becausethe location data on which the visit detection process operated toproduce the setting of block 1102 or the setting identified in block1104 may not be reliable, an adjustment may not be made only on thebasis of that data. Accordingly, in some embodiments in which theconsumer analytics system adjusts the polygon, the consumer analyticssystem may do so on the basis of multiple units of location data forwhich secondary indications indicate that the location data correspondsto a setting. A similar adjustment may be made with respect to operatingtimes of a setting and/or category of a setting, in embodiments in whicha definition of a setting includes an indication of operating timesand/or category.

Adjustments to a data set of settings that may be recognized by a visitdetection process may also be made in some embodiments. To adjust thedata set, settings may be added and/or removed from the data set. Forexample, when multiple units of location data are received fromconsumers and indicate the same general location that a visit detectioncannot match to a setting, this may be indicative of a new setting thatmay be added to the data set. When the consumer analytics systemidentifies that multiple units of location data indicating a samegeneral location are not matched to a setting, but secondary indicationsfor these units of location data are consistently indicating the samesetting visited by the consumers, the consumer analytics system mayprompt a consumer and/or an administrator to input data regarding thispotential new setting. The input data may include some or all of thedata defining the setting in the data set, including any of the examplesof data defining a setting that were discussed above. Similarly, if thevisit detection process consistently matches location data for alocation to a setting, but secondary indications for the locationindicate another setting, this may indicate that a setting at thelocation has changed. In response to making this detection, the consumeranalytics system may request that a consumer and/or an administratorprovide information identifying whether a previous setting has closedand should be removed from the data set and/or whether a new setting hasopened and should be added to the data set. If a new setting is to beadded to the data set, then the consumer and/or administrator may beprompted to input data regarding the new setting, including any of theexamples of data defining a setting that were discussed above.

Once the consumer analytics system performs the adjustment of block1110, the process 1100 ends.

As a result of the process 1100, the visit detection process of aconsumer analytics system is changed. The change in the visit detectionprocess may result in a visit detection process that is capable ofdetecting settings that the process was not previously capable ofdetecting, no longer capable of detecting settings that the process waspreviously capable of detecting, and/or more precise in its detection ofsettings visited by consumers.

Reporting

The results of all of the information created from consumer feedback andopinions, location data, and any other data sources from which data canbe collected can be presented in a software system which allows for easyanalysis and the ability to understand and evaluate the data. Theresults may be stored in a database (such as a SQL-based or OLAP DBMS)that may allow for easy exploration of the results by filtering resultsof the survey responses for each consumer, including according tobehaviors that have been determined for each consumer, or allowexploration of related data that has been added to the system, such asby being retrieved from one or more external data sources. In addition,various types of mathematical aggregation (e.g. sum, median, average,standard deviation, etc.) can be computed and displayed for each type ofdata that may be stored.

Example Use Case: Shopper Marketing Measurement

In this example, a consumer analytics system can be used to allowmarketing analysts to understand how effective their shopper marketingbudget is being deployed inside stores.

“Shopper marketing” can be defined as “brand marketing in a retailenvironment.” It includes things like special promotions, endcaps,in-store TV spots, promotional banners/signage/displays, positioning instore circulars, etc. Research has shown that 70% of brand selectionsare made by consumers in store, so vendors of consumer-packaged goods(CPGs) are increasingly focusing their efforts on in-store shoppermarketing.

In some embodiments, a consumer analytics system operating according totechniques described herein may be used to gaugeimpact/effectiveness/recall/awareness/reach of shopper marketing. Eventhough a CPG vendor may have a unified shopper marketing campaign acrossretailers, each retailer may to deploy that campaign differently. It maytherefore be difficult to gauge the impact across retailers or evenwithin different stores of the same retailer.

Conventional approaches to determining impact of shopper marketing maybe inadequate. For example, one conventional way to gauge impact is bylooking at sales figures from a panel of consumers who agree to manuallyenter the products they buy or from transaction logs. This may beinadequate because each sale is the result of many different types ofadvertising (TV, online, OOH, in-store, etc.). This also misses theimpact of competitors' advertising on purchasing decisions. As anotherexample, another way to get a handle on shopper marketing impact is theshop-along, in which a shopper is accompanied on a shopping trip by amarket researcher. Shop-alongs can be very expensive and often can onlyreach one or two stores, and may also require permission from the storeowners. Obtaining permission can take a prohibitively long time, andsome stores won't ever allow shop-alongs (e.g., ULTA® Beauty Stores, whohave been rated best-in-class for beauty products and reportedly won'tcollaborate with any market research firms).

A consumer analytics system of one embodiment, however, may beadvantageous in this circumstance. The system may first collect locationdata from a set of consumers (a “panel”). This location data may be usedby the system to build a profile for a consumer and also to allow thesystem to discover when a consumer visits one of the stores which are ofinterest in the market research study. When the system detects aconsumer has entered or is likely to enter a store of interest, thesystem may take one or more actions including request that the consumercarry out a task. For example, in return for a monetary incentive, theconsumers may be asked to go to a store of interest and, when in thestore, fill out a short survey on their mobile device. The survey mayinclude a question asking if a consumer is aware if the store they areshopping in (or recently shopped in) carries a given product and whattheir opinion is of that product. The survey may also include questionsasking the consumer for her impressions on the shopper marketingefforts, and which products the consumer has chosen to buy and why. Theconsumer analytics system may also take an action that is requestingthat the consumer capture some data regarding the business or a productpurchased by the consumer (e.g., take a photo, record a wireless signal,record a barcode/QR code/etc.). This information collected from theconsumer may be more closely related to the shopper marketing than datathat may be obtained using conventional techniques, and the data may beused by a market researcher to determine an impact of the shoppermarketing.

1. A method of processing a plurality of units of location data for aplurality of consumers to determine whether to solicit informationrelating to commercial activity, the location data identifying aplurality of locations of the plurality of consumers at a plurality oftimes, the method comprising: determining, using at least one processorand based at least in part on the plurality of units of location datafor the plurality of consumers, at least one behavior for one or more ofthe plurality of consumers; and in response to determining that the atleast one behavior satisfies at least one condition to solicit theinformation, sending at least one message to a consumer solicitinginformation relating to commercial activity.
 2. The method of claim 1,wherein: determining the at least one behavior for one or more of theplurality of consumers comprises determining that the consumer visited acommercial entity, wherein the determining is performedcontemporaneously with the visit by the consumer to the commercialentity; and sending at least one message to the consumer soliciting theinformation comprises sending, contemporaneously with the visit by theconsumer to the commercial entity, at least one message to the consumersoliciting information regarding the commercial entity.
 3. The method ofclaim 1, wherein sending the at least one message to the consumersoliciting the information comprises soliciting information from theconsumer regarding a commercial entity, product, and/or service.
 4. Themethod of claim 1, wherein sending the at least one message to theconsumer soliciting the information comprises sending at least onemessage to the consumer requesting that the consumer perform a taskcomprising interacting with a commercial entity and provide informationrelating to the consumer's interaction with the commercial entity. 5.The method of claim 1, wherein: the determining and the sending areperformed by a system that processes the plurality of units of locationdata, and sending the at least one message to the consumer solicitingthe information comprises requesting that the consumer provide theinformation to the system that processes the plurality of units oflocation data.
 6. The method of claim 1, wherein: location data of theplurality of units of location data indicating a location of a consumeris electronically-derived at a time the consumer is visiting a setting;and the determining and the sending are performed at times at which theconsumer is present at the setting.
 7. The method of claim 1, wherein:location data of the plurality of units of location data indicating alocation of a consumer is electronically-derived at a time the consumeris traveling toward a setting; and the sending is performed at a time atwhich the consumer is traveling toward the setting.
 8. The method ofclaim 1, wherein: determining the at least one behavior of the one ormore consumers comprises predicting a behavior of the consumer; andsending the at least one message in response to determining that the atleast one behavior satisfies the at least one condition comprisessending the at least one message in response to determining that thepredicted behavior satisfies the at least one condition.
 9. The methodof claim 1, wherein: determining the at least one behavior of the one ormore consumers comprises determining a behavior in which a firstconsumer was engaged at the time one or more units of location data werederived; and sending the at least one message to the consumer solicitingthe information comprises sending the at least one message to the firstconsumer.
 10. The method of claim 1, wherein: determining the at leastone behavior of the one or more consumers comprises determining abehavior of a first consumer; and sending the at least one message tothe consumer soliciting the information comprises sending the at leastone message to a second consumer different from the first consumer. 11.The method of claim 10, wherein determining the at least one behavior ofthe one or more consumers further comprises inferring and/or predictinga behavior of the second consumer based at least in part on thedetermined behavior of the first consumer.
 12. The method of claim 11,wherein inferring and/or predicting the behavior of the second consumercomprises: in response to determining the behavior of the firstconsumer, identifying one or more other consumers included in a group ofconsumers that includes the first consumer, consumers of the group ofconsumers having one or more characteristics in common, the one or moreother consumers including the second consumer.
 13. The method of claim1, wherein determining the at least one behavior for the one or moreconsumers based at least in part on the plurality of units of locationdata comprises determining the at least one behavior based at least inpart on a plurality of measurements of physical location for the one ormore consumers.
 14. The method of claim 1, wherein determining the atleast one behavior for the one or more consumers based at least in parton the plurality of units of location data comprises determining the atleast one behavior based at least in part on indications of location ofa consumer provided by the consumer to a social networking service. 15.The method of claim 1, wherein determining the at least one behavior forthe one or more consumers based at least in part on the plurality ofunits of location data comprises determining the at least one behaviorbased at least in part on indoor location data identifying a location ofa consumer inside a setting.
 16. The method of claim 1, whereindetermining the at least one behavior for the one or more consumerscomprises determining a behavior of a group of behaviors consisting ofvisiting a first setting and traveling past a second setting during asame trip, deviating from a previously-determined pattern of behavior,and traveling toward a setting.
 17. The method of claim 1, whereindetermining the at least one behavior for the one or more consumerscomprises determining that a first consumer is visiting or has visited asetting based on a plurality of location measurements for the firstconsumer.
 18. The method of claim 1, wherein determining the at leastone behavior for the one or more consumers comprises determining that afirst consumer is traveling past or has traveled past a setting.
 19. Themethod of claim 1, wherein determining the at least one behavior for theone or more consumers comprises: determining that a first consumerpreviously visited a first setting during a first trip; and determiningthat the first consumer visited a second setting during a second trip,wherein determining that the first consumer visited the second settingis performed contemporaneously with the first consumer's visit to thesecond setting.
 20. The method of claim 1, wherein determining the atleast one behavior for the one or more consumers comprises determiningthat a first consumer is engaging or has engaged in a behavior thatdeviates from a previously-determined pattern of behavior for the firstconsumer.
 21. The method of claim 1, wherein determining the at leastone behavior comprises determining at least one retail-relevant behaviorfor the one or more consumers.
 22. The method of claim 1, whereindetermining the at least one behavior comprises determining at least onelifestyle-relevant behavior for the one or more consumers.
 23. Themethod of claim 1, wherein sending the at least one message to theconsumer soliciting the information comprises transmitting, via acommunication network, the at least one message to a device operated bythe consumer.
 24. The method of claim 23, wherein: the device operatedby the consumer is a mobile device operated by the consumer; and themethod further comprises receiving location data of the plurality ofunits of location data from the mobile device operated by the consumer.25. The method of claim 1, wherein sending the at least one message tothe consumer soliciting the information comprises transmitting, via acommunication network, the at least one message using an e-mail addressfor the consumer.
 26. The method of claim 1, wherein sending the atleast one message to the consumer soliciting the information comprisesrequesting that the consumer respond with one or more answers to one ormore questions.
 27. The method of claim 26, wherein: determining atleast one behavior for one or more of the plurality of consumerscomprises determining that a first consumer visited or is visiting acommercial entity; and requesting that the consumer respond with one ormore answers to one or more questions comprises requesting that theconsumer answer one or more questions regarding in-store advertising atthe commercial entity.
 28. The method of claim 1, wherein sending the atleast one message to the consumer soliciting the information comprisesrequesting that the consumer to obtain media and upload the media, via acommunication network, to at least one computing device, a subject ofthe media being related to the commercial activity.
 29. The method ofclaim 28, wherein requesting that the consumer obtain the mediacomprises requesting that the consumer obtain an image, video, and/oraudio of a subject related to a commercial entity.
 30. The method ofclaim 29, wherein requesting that the consumer obtain the image, video,and/or audio of a subject that is related to a commercial entitycomprises requesting that the consumer obtain an image of a part of astore.
 31. The method of claim 1, wherein sending the at least onemessage to the consumer soliciting the information comprises requestingthat the consumer provide information regarding one or more itemspurchased by the consumer.
 32. The method of claim 1, wherein sendingthe at least one message to the consumer soliciting the informationcomprises requesting that the consumer scan a UPC, QR code, and/or NFCtag and provide information obtained from the scan.
 33. The method ofclaim 1, further comprising: in response to receiving the solicitedinformation from the consumer, providing at least one incentive to theconsumer.
 34. The method of claim 1, wherein: sending the at least onemessage to the consumer comprises sending the at least one message to aplurality of selected consumers; and the method further comprises:predicting and/or inferring, using the at least one processor and basedat least in part on the plurality of units of location data for theplurality of consumers, at least one characteristic of consumers of theplurality of selected consumers, the predicting and/or inferringcomprising predicting and/or inferring a first characteristic for afirst set of consumers of the plurality of selected consumers; receivinga second plurality of units of information from the plurality ofselected consumers in response to the at least one message; andanalyzing the second plurality of units of information received from theplurality of selected consumers to determine, from the second pluralityof units of information, attributes of information provided by consumersof the first set for which the first characteristic was predicted and/orinferred.
 35. A method of determining, based on location data, whetherto solicit information relating to commercial activity, the methodcomprising: receiving, over a period of time, a plurality of units oflocation data, the plurality of units of location data identifying aplurality of locations of the plurality of consumers at a plurality oftimes, the plurality of units of location data comprising multiplemeasurements of a physical location of a first consumer; analyzing theplurality of units of location data and producing a result of theanalysis; determining, using at least one processor, whether the resultof the analysis satisfies a condition for information to be solicited;and in response to determining that the result satisfies the condition,sending at least one message to a consumer soliciting informationrelating to commercial activity of the first consumer.
 36. The method ofclaim 35, wherein: the multiple measurements of the physical location ofthe first consumer are electronically-derived measurements derived at atime the first consumer is visiting a setting; and the determining andthe sending are performed contemporaneous with the first consumer beingpresent at the setting.
 37. The method of claim 35, further comprising:when it is determined that the result does not satisfy the condition,refraining from sending the at least one message to the first consumer.38. The method of claim 35, wherein analyzing the plurality of units oflocation data and producing a result of the analysis comprises analyzingthe plurality of units of location data to determine at least onecharacteristic of consumers of the plurality of consumers.
 39. Themethod of claim 38, wherein analyzing the plurality of units of locationdata to determine the at least one characteristic comprises analyzingthe plurality of units of location data to determine an identitycharacteristic of the first consumer, a behavior characteristic of thefirst consumer, and/or a preference characteristic of the firstconsumer.
 40. The method of claim 35, wherein: analyzing the pluralityof units of location data comprises determining, using at least oneprocessor, at least one behavior for one or more of the plurality ofconsumers; and sending the at least one message to the first consumer inresponse to determining that the result of the analysis satisfies thecondition comprises sending the at least one message in response todetermining that the at least one behavior satisfies at least onecondition for soliciting the information.
 41. The method of claim 40,wherein: location data of the plurality of units of location dataindicating a location of a consumer is electronically-derived at a timethe first consumer is visiting a setting; and the determining and thesending are performed at times at which the first consumer is present atthe setting.
 42. The method of claim 40, wherein: determining the atleast one behavior of the one or more consumers comprises determining abehavior in which a first consumer was engaged at the time one or moreunits of location data were derived.
 43. The method of claim 40,wherein: determining the at least one behavior of the one or moreconsumers comprises determining a behavior of a second consumer, thesecond consumer being different from the first consumer.
 44. The methodof claim 40, wherein determining the at least one behavior for the oneor more consumers comprises determining a behavior of a group ofbehaviors consisting of visiting a setting, traveling past a setting,visiting a first setting and traveling past a second setting during asame trip, deviating from a previously-determined pattern of behavior,and traveling toward a setting.
 45. The method of claim 35, whereinsending the at least one message to the first consumer soliciting theinformation comprises requesting that the first consumer perform a taskof a group of tasks comprising requesting that the first consumerrespond with one or more answers to one or more questions, requestingthat the first consumer to obtain media of a subject relating tocommercial activity and upload the media, via a communication network,to at least one computing device, and requesting that the first consumerscan a UPC, QR code, and/or NFC tag and provide information obtainedfrom the scan.
 46. The method of claim 35, wherein: sending the at leastone message to the first consumer comprises sending the at least onemessage to a plurality of selected consumers; and the method furthercomprises: predicting and/or inferring, using the at least one processorand based at least in part on the plurality of units of location datafor the plurality of consumers, at least one characteristic of consumersof the plurality of selected consumers, the predicting and/or inferringcomprising predicting and/or inferring a first characteristic for afirst set of consumers of the plurality of selected consumers; receivinga plurality units of information from the plurality of selectedconsumers in response to the at least one message; and analyzing theplurality of units of information received from the plurality ofselected consumers to determine, from the plurality of units ofinformation, attributes of information provided by consumers of thefirst set for which the first characteristic was predicted and/orinferred.
 47. An apparatus comprising: at least one processor; and atleast one storage medium having encoded thereon executable instructionsthat, when executed by the at least one processor, cause the at leastone processor to carry out a method of processing a plurality of unitsof location data for a plurality of consumers to determine whether tosolicit information relating to commercial activity, the location dataidentifying a plurality of locations of the plurality of consumers at aplurality of times, the method comprising: determining, using at leastone processor and based at least in part on the plurality of units oflocation data for the plurality of consumers, at least one behavior forone or more of the plurality of consumers; and in response todetermining that the at least one behavior satisfies at least onecondition to solicit the information, sending at least one message to aconsumer soliciting information relating to commercial activity.
 48. Theapparatus of claim 47, wherein: determining the at least one behaviorfor one or more of the plurality of consumers comprises determining thatthe consumer visited a commercial entity, wherein the determining isperformed contemporaneously with the visit by the consumer to thecommercial entity; and sending at least one message to the consumersoliciting the information comprises sending, contemporaneously with thevisit by the consumer to the commercial entity, at least one message tothe consumer soliciting information regarding the commercial entity. 49.The apparatus of claim 47, wherein sending the at least one message tothe consumer soliciting the information comprises sending at least onemessage to the consumer requesting that the consumer perform a taskcomprising interacting with a commercial entity and provide informationrelating to the consumer's interaction with the commercial entity. 50.The apparatus of claim 47, wherein: determining the at least onebehavior of the one or more consumers comprises predicting a behavior ofthe consumer; and sending the at least one message in response todetermining that the at least one behavior satisfies the at least onecondition comprises sending the at least one message in response todetermining that the predicted behavior satisfies the at least onecondition.
 51. The apparatus of claim 47, wherein: determining the atleast one behavior of the one or more consumers comprises determining abehavior in which a first consumer was engaged at the time one or moreunits of location data were derived; and sending the at least onemessage to the consumer soliciting the information comprises sending theat least one message to the first consumer.
 52. The apparatus of claim47, wherein: determining the at least one behavior of the one or moreconsumers comprises determining a behavior of a first consumer; andsending the at least one message to the consumer soliciting theinformation comprises sending the at least one message to a secondconsumer different from the first consumer.
 53. The apparatus of claim47, wherein determining the at least one behavior for the one or moreconsumers based at least in part on the plurality of units of locationdata comprises determining the at least one behavior based at least inpart on a plurality of measurements of physical location for the one ormore consumers.
 54. The apparatus of claim 47, wherein determining theat least one behavior for the one or more consumers comprisesdetermining a behavior of a group of behaviors consisting of visiting asetting, traveling past a setting, visiting a first setting andtraveling past a second setting during a same trip, deviating from apreviously-determined pattern of behavior, and traveling toward asetting.
 55. The apparatus of claim 47, wherein: sending the at leastone message to the consumer comprises sending the at least one messageto a plurality of selected consumers; and the method further comprises:predicting and/or inferring, using the at least one processor and basedat least in part on the plurality of units of location data for theplurality of consumers, at least one characteristic of consumers of theplurality of selected consumers, the predicting and/or inferringcomprising predicting and/or inferring a first characteristic for afirst set of consumers of the plurality of selected consumers; receivinga second plurality of units of information from the plurality ofselected consumers in response to the at least one message; andanalyzing the second plurality of units of information received from theplurality of selected consumers to determine, from the second pluralityof units of information, attributes of information provided by consumersof the first set for which the first characteristic was predicted and/orinferred.
 56. A method of operating a portable computing device, themethod comprising: obtaining a plurality of units of location data, eachof the plurality of units of location data indicating a location of aconsumer determined as the consumer moved while engaging in at least onebehavior; transmitting the plurality of units of location data to atleast one server; receiving, from the at least one server, at least onemessage soliciting information relating to commercial activity, the atleast one message having been transmitted by the at least one server atleast partly in response to the at least one server determining the atleast one behavior of the consumer based at least in part on theplurality of units of location data; receiving, from the consumer via auser interface of the portable computing device, the informationsolicited by the at least one message; and transmitting the informationreceived from the consumer to the at least one server.
 57. The method ofclaim 56, further comprising: displaying to the consumer, via the userinterface of the portable computing device, a description of theinformation solicited by the at least one message.
 58. The method ofclaim 56, wherein receiving the at least one message solicitinginformation relating to the commercial activity comprises receiving atleast one message requesting that the consumer perform a task of a groupof tasks comprising requesting that the consumer provide one or moreanswers to one or more questions to the at least one server, requestingthat the consumer to obtain media of a subject relating to commercialactivity and upload the media, via a communication network, to the atleast one server, and requesting that the consumer scan a UPC, QR code,and/or NFC tag and provide information obtained from the scan to the atleast one server.
 59. The method of claim 56, wherein obtaining theplurality of units of location data comprises measuring a physicallocation of the consumer contemporaneously with the consumer beingpresent at a setting.
 60. The method of claim 59, wherein receiving theinformation solicited by the at least one message from the consumercomprises receiving the information contemporaneously with the consumerbeing present at the setting.
 61. The method of claim 60, whereinreceiving the at least one message soliciting the information relatingto commercial activity comprises receiving at least one messagesoliciting information relating to the setting, wherein the setting is acommercial entity.
 62. The method of claim 61, wherein receiving atleast one message soliciting information relating to the commercialentity comprises receiving information relating to opinions of theconsumer regarding the commercial entity.
 63. The method of claim 61,wherein receiving at least one message soliciting information relatingto the commercial entity comprises receiving information relating toobservations of the consumer regarding in-store marketing at thecommercial entity.
 64. The method of claim 56, wherein obtaining theplurality of units of location data that each indicate a location of theconsumer determined as the consumer moved while engaging in the at leastone behavior comprises obtaining the plurality of units of location datathat each indicate a location of the consumer determined as the consumerengaged in a behavior of a set of behaviors consisting of visiting asetting, traveling past a setting, visiting a first setting andtraveling past a second setting during a same trip, deviating from apreviously-determined pattern of behavior, and traveling toward asetting.
 65. The method of claim 56, wherein obtaining the plurality ofunits of location data comprises: measuring a physical location of theconsumer as the consumer engages in the at least one behavior; andidentifying a setting corresponding to the physical location.
 66. Themethod of claim 65, wherein the acts of measuring and identifying areperformed by the portable computing device.
 67. The method of claim 65,wherein identifying a setting corresponding to the physical locationcomprises comparing the physical location to at least one definition ofat least one setting of a set of settings stored on at least one storagemedium of the portable computing device.